Abdelkader Nasreddine Belkacem

Abdelkader Nasreddine Belkacem

United Arab Emirates University

H-index: 18

Asia-United Arab Emirates

Abdelkader Nasreddine Belkacem Information

University

United Arab Emirates University

Position

PhD from Tokyo Institute of Technology Assistant Professor at

Citations(all)

1196

Citations(since 2020)

1090

Cited By

343

hIndex(all)

18

hIndex(since 2020)

18

i10Index(all)

29

i10Index(since 2020)

29

Email

University Profile Page

United Arab Emirates University

Abdelkader Nasreddine Belkacem Skills & Research Interests

Brain-computer Interface

Human-robot Interaction

Computational Neuroscience

MEG/EEG

Applied AI

Top articles of Abdelkader Nasreddine Belkacem

Classification and transfer learning of sleep spindles based on convolutional neural networks

Background Sleep plays a critical role in human physiological and psychological health, and electroencephalography (EEG), an effective sleep-monitoring method, is of great importance in revealing sleep characteristics and aiding the diagnosis of sleep disorders. Sleep spindles, which are a typical phenomenon in EEG, hold importance in sleep science. Methods This paper proposes a novel convolutional neural network (CNN) model to classify sleep spindles. Transfer learning is employed to apply the model trained on the sleep spindles of healthy subjects to those of subjects with insomnia for classification. To analyze the effect of transfer learning, we discuss the classification results of both partially and fully transferred convolutional layers. Results The classification accuracy for the healthy and insomnia subjects’ spindles were 93.68% and 92.77%, respectively. During transfer learning, when transferring all convolutional layers, the classification accuracy for the insomnia subjects’ spindles was 91.41% and transferring only the first four convolutional layers achieved a classification result of 92.80%. The experimental results demonstrate that the proposed CNN model can effectively classify sleep spindles. Furthermore, the features learned from the data of the normal subjects can be effectively applied to the data for subjects with insomnia, yielding desirable outcomes. Discussion These outcomes underscore the efficacy of both the collected dataset and the proposed CNN model. The proposed model exhibits potential as a rapid and effective means to diagnose and treat sleep disorders, thereby improving the speed and quality of patient care.

Authors

Jun Liang,Abdelkader Nasreddine Belkacem,Yanxin Song,Jiaxin Wang,Zhiguo Ai,Xuanqi Wang,Jun Guo,Lingfeng Fan,Changming Wang,Bowen Ji,Zengguang Wang

Journal

Frontiers in Neuroscience

Published Date

2024/4/24

Increasing Accessibility to a Large Brain-Computer Interface Dataset: Curation of Physionet EEG Motor Movement/Imagery Dataset for Decoding and Classification

A reliable motor imagery (MI) brain-computer interface (BCI) requires accurate decoding, which in turn requires model calibration using electroencephalography (EEG) signals from subjects executing or imagining the execution of movements. Although the PhysioNet EEG Motor Movement/Imagery Dataset is currently the largest EEG dataset in the literature, relatively few studies have used it to decode MI trials. In the present study, we curated and cleaned this dataset to store it in an accessible format that is convenient for quick exploitation, decoding, and classification using recent integrated development environments. We dropped six subjects owing to anomalies in EEG recordings and pre-possessed the rest, resulting in 103 subjects spanning four MI and four motor execution tasks. The annotations were coded to correspond to different tasks using numerical values. The resulting dataset is stored in both MATLAB …

Authors

Zaid Shuqfa,Abderrahmane Lakas,Abdelkader Nasreddine Belkacem

Journal

Data in Brief

Published Date

2024/2/12

Face mask for accurate location of sensors relative to a users face, a communication enabling face mask and a communication system including the face mask

Face mask communication system 100 includes face mask 10 worn by user 14 and signal receiving hand glove 16 worn by user 18. Glove 16 includes data receiver 66 for data communication with mask 10 and includes multiple vibrotactile devices for generating haptic signals. Mask 10 includes an elastic element of flexible material, and a plurality of EMG sensors 12 fixed to the element, for sensing electrical activity of face regions of the user's 14 face. Mask 10 includes a processor 60; decoding algorithm 110 and transmitter 62 for, respectively, processing signals from the sensors 12; generating command instructions based on the signals; and wirelessly transmitting the signals to receiver 66 of glove 16. Mask 10 includes thread elements connected to the elastic element of mask 10 enabling tensioning of the element to provide for fitment of mask 10 to users of different sizes, for optimal location sensors 12.

Published Date

2023/2/7

Convolutional Neural Network for Emotional EEG Decoding and Visualization

In recent years, deep learning has been increasingly utilized in affective Brain-Computer Interface (aBCI) research. The application of Convolutional Neural Networks (ConvNets) for end-to-end analysis of electroencephalographic (EEG) signals has become a common approach in deep learning-based aBCI. However, limited research has been conducted on a better understanding of how to design and train ConvNets for end-to-end emotional EEG decoding. This study explores three kinds of ConvNets architectures, including shallow, middle, and deep configuration, to evaluate their design and training schemes. The findings of this paper demonstrate that, for aBCI, it is crucial to ensure an adequate sample size for model training while maintaining the stability of EEG signals. Additionally, achieving a balance between sample length and size is crucial for effective model training. Notably, EEGNet outperforms the …

Authors

Jiaying Lin,Lu Li,Abdelkader Nasreddine Belkacem,Jun Liang,Chao Chen

Published Date

2023/10/27

Decoding multi-class motor imagery and motor execution tasks using Riemannian geometry algorithms on large EEG datasets

The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain–computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise and nonstationarity of electroencephalography signals. However, the related literature shows high classification accuracy on only relatively small BCI datasets. The aim of this paper is to provide a study of the performance of a novel implementation of the Riemannian geometry decoding algorithm using large BCI datasets. In this study, we apply several Riemannian geometry decoding algorithms on a large offline dataset using four adaptation strategies: baseline, rebias, supervised, and unsupervised. Each of these adaptation strategies is applied in motor execution and motor imagery for both scenarios 64 electrodes and 29 electrodes. The dataset is composed of four-class bilateral and unilateral motor imagery and motor execution of 109 subjects. We run several classification experiments and the results show that the best classification accuracy is obtained for the scenario where the baseline minimum distance to Riemannian mean has been used. The mean accuracy values up to 81.5% for motor execution, and up to 76.4% for motor imagery. The accurate classification of EEG trials helps to realize successful BCI applications that allow effective control of devices.

Authors

Zaid Shuqfa,Abdelkader Nasreddine Belkacem,Abderrahmane Lakas

Journal

Sensors

Published Date

2023/5/25

Diagnosis of Schizophrenia from EEG signals Using ML Algorithms

Early treatment is required to control the symptoms and serious complications caused by schizophrenia (SZ). People suffering from SZ require lifelong treatment. The use of machine learning (ML) models to detect various health problems such as SZ has received considerable attention from researchers in recent years. This study investigated the effectiveness of various ML models to detect and predict SZ using electroencephalogram data. A dataset of 14 healthy schizophrenic patients was used, and 12 features were extracted after applying independent component analysis. Three traditional ML models (logistic regression, support vector machine, and K-nearest neighbors) and a convolutional neural network (CNN) were trained, and their performance was compared. Results demonstrated that the CNN model outperformed the other three models with the highest accuracy score of 95% on validation data. Our …

Authors

Tariq Qayyum,Assadullah Tariq,Mohamed Adel Serhani,Zouheir Trabelsi,Abdelkader Nasreddine Belkacem

Published Date

2023/12/5

On Closed-Loop Brain Stimulation Systems for Improving the Quality of Life of Patients with Neurological Disorders

Emerging brain technologies have significantly transformed human life in recent decades. For instance, the closed-loop brain-computer interface (BCI) is an advanced software-hardware system that interprets electrical signals from neurons, allowing communication with and control of the environment. The system then transmits these signals as controlled commands and provides feedback to the brain to execute specific tasks. This paper analyzes and presents the latest research on closed-loop BCI that utilizes electric/magnetic stimulation, optogenetic, and sonogenetic techniques. These techniques have demonstrated great potential in improving the quality of life for patients suffering from neurodegenerative or psychiatric diseases. We provide a comprehensive and systematic review of research on the modalities of closed-loop BCI in recent decades. To achieve this, the authors used a set of defined criteria to shortlist studies from well-known research databases into categories of brain stimulation techniques. These categories include deep brain stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, transcranial alternating-current stimulation, and optogenetics. These techniques have been useful in treating a wide range of disorders, such as Alzheimer's and Parkinson's disease, dementia, and depression. In total, 76 studies were shortlisted and analyzed to illustrate how closed-loop BCI can considerably improve, enhance, and restore specific brain functions. The analysis revealed that literature in the area has not adequately covered closed-loop BCI in the context of cognitive neural prosthetics and implanted …

Authors

Abdelkader Nasreddine Belkacem,Jamil Nuraini,Sumayya Khalid,Fady Alnajjar

Journal

Frontiers in Human Neuroscience

Published Date

2023

On Neurodevelopmental Disorder based on Brain Computer Interface for Enhancing the Learning Process

This paper discusses neurodevelopmental disorders and their effects on the brain and nervous system. Autism, attention deficit hyperactivity disorder (ADHD), dyslexia, and cerebral palsy are just a few examples of the many conditions classified as neurodevelopmental disorders. These conditions can potentially influence wide-ranging facets of a person's life, including communication skills, behavior, ability to learn (and thus overall educational achievement), and motor skills. Hereditary and environmental variables are thought to contribute to the development of various developmental disorders, although the exact causes are unknown. The effects of neurodevelopmental disorders on a student's educational opportunities are highly variable according to the particular condition and specific requirements of the individual. Students with dyslexia, for instance, may have trouble reading and writing, whereas those with …

Authors

Nuraini Jamil,Omar Samir Alawa,Saad Mohammed Manar,Saeed Alawi Alaidarous,Abdulrahman Saeed Adam,Shehab Adel Eldemerdash,Abdelkader Nasreddine Belkacem

Published Date

2023/10/18

Improving students' cognitive abilities in online environment based on neurofeedback

The brain-computer interface (BCI) and eye-tracking technologies can potentially improve the learning environment in education. Cognitive BCIs can give a deep knowledge of brain functioning, enabling the creation of more effective learning approaches and improving brain-based abilities. This study proposes a neurofeedback strategy based on BCI and eye-tracking to collect factual data (monitoring students' brainwaves and eye movement) and analyze their cognitive capacities during online learning. This study aims to create patterns regarding students' learning behavior based on brain and eye movement responses to learning activities as part of the learning environment. As a result, teachers may adapt to new pedagogical ideas and a more flexible delivery style.

Authors

Nuraini Jamil,Abderrahmane Lakas,Abdelkader Nasreddine Belkacem

Published Date

2023/5/1

EEG-based epileptic seizure pattern decoding using vision transformer

Epilepsy is a prevalent neurological disorder and has been studied through the analysis of Electroencephalogram (EEG) signals. However, the identification and classification of epileptic seizure patterns remains challenging due to the non-stationary nature of EEG signals and the presence of artifacts. In this paper, we investigate the applicability of a transformer-based deep learning model to classify seizure patterns observed in epileptic patients. We employed the self-attention mechanism inherent in transformers to capture complex temporal relationships in the EEG recordings. By prepossessing the EEG signals into suitable input sequences and adapting the transformer architecture, we achieved 78.11% in distinguishing between different epileptic seizure patterns. Our findings indicate that the transformer model, with its ability to manage long-range dependencies, offers a robust approach to EEG-based seizure …

Authors

Abdelhadi Hireche,Rafat Damseh,Parikshat Sirpal,Abdelkader Nasreddine Belkacem

Published Date

2023/11/14

Hierarchical fusion detection algorithm for sleep spindle detection

Background Sleep spindles are a vital sign implying that human beings have entered the second stage of sleep. In addition, they can effectively reflect a person’s learning and memory ability, and clinical research has shown that their quantity and density are crucial markers of brain function. The “gold standard” of spindle detection is based on expert experience; however, the detection cost is high, and the detection time is long. Additionally, the accuracy of detection is influenced by subjectivity. Methods To improve detection accuracy and speed, reduce the cost, and improve efficiency, this paper proposes a layered spindle detection algorithm. The first layer used the Morlet wavelet and RMS method to detect spindles, and the second layer employed an improved k-means algorithm to improve spindle detection efficiency. The fusion algorithm was compared with other spindle detection algorithms to prove its effectiveness. Results The hierarchical fusion spindle detection algorithm showed good performance stability, and the fluctuation range of detection accuracy was minimal. The average value of precision was 91.6%, at least five percentage points higher than other methods. The average value of recall could reach 89.1%, and the average value of specificity was close to 95%. The mean values of accuracy and F1-score in the subject sample data were 90.4 and 90.3%, respectively. Compared with other methods, the method proposed in this paper achieved significant improvement in terms of precision, recall, specificity, accuracy, and F1-score. Conclusion A spindle detection method with high steady-state accuracy and fast detection speed is …

Authors

Chao Chen,Jiayuan Meng,Abdelkader Nasreddine Belkacem,Lin Lu,Weibo Yi,Penghai Li,Jun Liang,Zhaoyang Huang,Dong Ming

Journal

Frontiers in Neuroscience

Published Date

2023

AI in Education: Improving Quality for Both Centralized and Decentralized Frameworks

Education is essential for achieving many Sustainable Development Goals (SDGs). Therefore, the education system focuses on empowering more educated people and improving the quality of the education system. One of the latest technologies to enhance the quality of education is Artificial Intelligence (AI)-based Machine Learning (ML). As a result, ML has a significant influence on the education system. ML is currently widely applied in the education system for various tasks, such as creating models by monitoring student performance and activities that accurately predict student outcomes, their engagement in learning activities, decision-making, problem-solving capabilities, etc. In this research, we provide a survey of machine learning frameworks for both distributed (clusters of schools and universities) and centralized (university or school) educational institutions to predict the quality of students' learning …

Authors

Nisha Thorakkattu Madathil,Saed Alrabaee,Mousa Al-Kfairy,Rafat Damseh,Abdelkader N Belkacem

Published Date

2023/5/1

Investigating online searching behavior based on Google Trends in MENA Region before and after COVID-19

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in …

Authors

Abdelkader Nasreddine Belkacem,Nuraini Jamil,Saed Alrabaee

Published Date

2023/10/18

Robust Detection of Adversarial Attacks for EEG-based Motor Imagery Classification using Hierarchical Deep Learning

Electroencephalography (EEG) signal finds extensive use in various medical diagnoses and non-invasive brain computer interface (BCI) applications. These applications include assisting individuals with disabilities, operating devices, and facilitating communication with their environments. Recent EEG studies have achieved successful decoding of neural activity using only time series data, surpassing the classification accuracy achieved by human experts. However, the decoding models are susceptible to adversarial examples that remain imperceptible to human evaluation. Thus, there is a current lack of a versatile architecture capable of simultaneously detecting adversarial examples and classifying EEG data. In this paper, we explore a hierarchical neural network-based classifier and introduce an adversarial training approach to enable the first classifier to learn from both clean and adversarial EEG data …

Authors

Nour El Houda Sayah Ben Aissa,Abderrahmane Lakas,Ahmed Korichi,Chaker Abdelaziz Kerrache,Abdelkader Nasreddine Belkacem

Published Date

2023/11/14

A comparative analysis of sleep spindle characteristics of sleep-disordered patients and normal subjects

Spindles differ in density, amplitude, and frequency, and these variations reflect different physiological processes. Sleep disorders are characterized by difficulty in falling asleep and maintaining sleep. In this study, we proposed a new spindle wave detection algorithm, which was more effective compared with traditional detection algorithms such as wavelet algorithm. Besides, we recorded EEG data from 20 subjects with sleep disorders and 10 normal subjects, and then we compared the spindle characteristics of sleep-disordered subjects and normal subjects (those without any sleep disorder) to assess the spindle activity during human sleep. Specifically, we scored 30 subjects on the Pittsburgh Sleep Quality Index and then analyzed the association between their sleep quality scores and spindle characteristics, reflecting the effect of sleep disorders on spindle characteristics. We found a significant correlation between the sleep quality score and spindle density (p = 1.84 × 10−8, p-value <0.05 was considered statistically significant.). We, therefore, concluded that the higher the spindle density, the better the sleep quality. The correlation analysis between the sleep quality score and mean frequency of spindles yielded a p-value of 0.667, suggesting that the spindle frequency and sleep quality score were not significantly correlated. The p-value between the sleep quality score and spindle amplitude was 1.33 × 10−4, indicating that the mean amplitude of the spindle decreases as the score increases, and the mean spindle amplitude is generally slightly higher in the normal population than in the sleep-disordered population. The normal and sleep …

Authors

Chao Chen,Kun Wang,Abdelkader Nasreddine Belkacem,Lin Lu,Weibo Yi,Jun Liang,Zhaoyang Huang,Dong Ming

Journal

Frontiers in Neuroscience

Published Date

2023

Mobile Edge Computing Enabled Internet of Unmanned Things

Mobile edge computing (MEC) is an emerging technology which is becoming an important component in the networking infrastructure supporting Unmanned Aerial Vehicles (UAV). MEC paradigm contributes significantly to the reduction of communication latency and by allowing the presence of cloud-like services such as computation and storage at the edge of the network. UAVs can be viewed as flying Internet of Things (IoT) devices characterized by high mobility and energy scarcity. Therefore, MEC functionalities play an important role in addressing UAV application requirements in terms of resource allocation, task offloading, and energy efficiency optimization. In this chapter, we provide an architectural and functional overview of edge computing for UAV applications deployed in the context of IoT. We review the elements of MEC-assisted IoUT systems, the functionalities offered by MEC to UAV applications, the …

Authors

Abderrahmane Lakas,Abdelkader Nasreddine Belkacem,Parag Kulkarni

Published Date

2023/4/29

Wearable Device for Drowsy User Detection

This paper proposes a wearable acquisition device for noninvasive brain–computer interface-based drowsy driving detection, which is a major cause of automobile accidents. The device detects changes in brain activity and eye movements that indicate drowsiness by monitoring electrical signals obtained using electroencephalography, electrooculography, and muscle activity. This hardware device is intended to be low-cost and robust. The acquired signals (electroencephalography, electrooculography, and muscle activity) are accurate and noise-free, allowing the detection of electrical activity fluctuations when blinking, moving the head, or biting the teeth. This device can prevent car accidents caused by drowsy driving by providing real-time alerts to sleepy drivers. The proposed solution is low-cost. It is based on machine learning and monitors electroencephalography signals. It presents a promising approach to …

Authors

Solomon Ghebretatios,Hermon Teklesenbet,Muluberhan Woga,Naod Yemane,Abdelkader Nasreddine Belkacem

Published Date

2023/11/14

Brain–Computer Interface‐based Predator–Prey Drone Interactions

This chapter proposes a drone control method based on brain–computer interface (BCI) to determine the flight path in a predator–prey situation to seek enemy drones and track them. We have conducted two experiments both with a common BCI‐based control: the first one consists of virtual simulation platform with advanced environment allowing multiple drone control scenarios. The second one consists of an experimental testbed using real drones moving in an indoor environment. In both experimental settings, we use Electroencephalography ( EEG ) recording system with eight dry electrodes, where the able‐bodied subjects are instructed to control the drones in real‐time using P300 paradigm and EEG Unicorn Hybrid Black system. For the drone simulation experiment, the participants are instructed to sit in front …

Authors

Abdelkader Nasreddine Belkacem,Abderrahmane Lakas

Journal

Handbook of Human‐Machine Systems

Published Date

2023/7/19

On enhancing students’ cognitive abilities in online learning using brain activity and eye movements

The COVID-19 pandemic has interrupted education institutions in over 150 nations, affecting billions of students. Many governments have forced a transition in higher education from in-person to remote learning. After this abrupt, worldwide transition away from the classroom, some question whether online education will continue to grow in acceptance in post-pandemic times. However, new technology, such as the brain-computer interface and eye-tracking, have the potential to improve the remote learning environment, which currently faces several obstacles and deficiencies. Cognitive brain computer interfaces can help us develop a better understanding of brain functions, allowing for the development of more effective learning methodologies and the enhancement of brain-based skills. We carried out a systematic literature review of research on the use of brain computer interfaces and eye-tracking to measure …

Authors

Nuraini Jamil,Abdelkader Nasreddine Belkacem,Abderrahmane Lakas

Published Date

2023/4

NewsGPT: ChatGPT Integration for Robot-Reporter

The integration of large language models (LLMs) with social robots has emerged as a promising avenue for enhancing human-robot interactions at a time when news reports generated by artificial intelligence (AI) are gaining in credibility. This integration is expected to intensify and become a more productive resource for journalism, media, communication, and education. In this paper a novel system is proposed that integrates AI's generative pretrained transformer (GPT) model with the Pepper robot, with the aim of improving the robot's natural language understanding and response generation capabilities for enhanced social interactions. By leveraging GPT's powerful language processing capabilities, this system offers a comprehensive pipeline that incorporates voice input recording, speech-to-text transcription, context analysis, and text-to-speech synthesis action generation. The Pepper robot is enabled to comprehend user queries, generate informative responses with general knowledge, maintain contextually relevant conversations, and act as a more domain-oriented news reporter. It is also linked with a news resource and powered with a Google search capability. To evaluate the performance of the framework, experiments were conducted involving a set of diverse questions. The robot's responses were assessed on the basis of eight criteria, including relevance, context, and fluency. Despite some identified limitations, this system contributes to the field of journalism and human-robot interaction by showcasing the potential of integrating LLMs with social robots. The proposed framework opens up opportunities for improving the …

Authors

Abdelhadi Hireche,Abdelkader Nasreddine Belkacem,Sadia Jamil,Chao Chen

Journal

arXiv preprint arXiv:2311.06640

Published Date

2023/11/11

WPT-enabled multi-UAV path planning for disaster management deep Q-network

Unmanned aerial vehicles (UAVs) have been more prevalent over the past several years with the intent to be widely deployed in many industries, including agriculture, cinematography, healthcare, delivery, and disaster management missions due to their ability to provide real-time situational awareness. However, various limitations such as the battery capacity, the charging method, and the flying range make it difficult for most applications to carry out routine tasks in vast areas. In this paper, a deep reinforcement learning (DRL) method for multi-UAV path planning that considers a cooperative action amongst UAVs in which they share the next destination to avoid visiting the same location at the same time. The Deep Q-Network algorithm (DQN) enables UAVs to autonomously plan their fastest path and ensure the continuity of the mission by deciding when to schedule a visit to a charging station or a data collection …

Authors

Adel Merabet,Abderrahmane Lakas,Abdelkader Nasreddine Belkacem

Published Date

2023/6/19

Vehicle Auto-Classification Using Machine Learning Algorithms Based on Seismic Fingerprinting

Most vehicle classification systems now use data from images or videos. However, these approaches violate drivers’ privacy and reveal their identities. Due to various disruptions, detecting automobiles using seismic ambient noise signals is challenging. This study uses seismic surface waves to compare time series data between different vehicle types. We applied various artificial intelligence approaches using raw data from three different vehicle sizes (Bus/Truck, Car, and Motorcycle) and background noise. By using vertical component seismic data, this study compares the decoding abilities of Logistic Regression, Support Vector Machine, and Naïve Bayes (NB) approaches to determine the class of automobiles. The Multiclass classifiers were trained on 4185 samples and tested on 1395 randomly chosen from actual and synthetic data sets. Additionally, we used the convolutional neural network approach as a baseline to assess the effectiveness of machine learning (ML) methods. The NB method showed relatively high classification accuracy during training for the three multiclass classification situations. Overall, we investigate an ML-based decoding technique that can be used for security and traffic analysis across large geographic areas without endangering driver privacy and is more effective and economical than conventional methods.

Authors

Ahmad Bahaa Ahmad,Hakim Saibi,Abdelkader Nasreddine Belkacem,Takeshi Tsuji

Journal

Computers

Published Date

2022/9/30

Real-time Control of UGV Robot in Gazebo Simulator using P300-based Brain-Computer Interface

Brain computer interface (BCI)-based virtual environment control has found broad applications in solving and pursuing factual healthcare issues concerning efficiency, safety, and costs. In this technical paper, an unmanned ground vehicle (UGV) robot with a simulator-equipped BCI system was utilized. The Gazebo simulator was employed to develop a simulated setting. The software CitySim World allowed rendering the simulated milieu more down-to-earth. A non-invasive electroencephalogram (EEG)-based BCI was used to follow the brain signals and extract the P300 component, a kind of simultaneous BCI controlling procedure for safe, fast, and inexpensive implementation. This UGV control system using human brain activity can be beneficial for the real UGV platform control. It enables the discovery of the probable errors in the physical implementation. All the steps implementing our BCI system were …

Authors

Fatima Ali Al Nuaimi,Jamal Zeddoug,Abdelkader Nasreddine Belkacem

Published Date

2022/12/6

A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders

BackgroundEarly and accurate diagnosis of bipolar and major depressive disorders is important in clinical practice. However, no diagnostic biomarkers can discriminate bipolar from major depressive disorder with high accuracy at present.MethodsWe propose a novel convolutional neural network architecture using multichannel raw resting-state electroencephalograph signals to differentiate bipolar disorder from major depressive disorder. This method has great potential in diagnosing mental disorders. In total, 101 patients with major depressive disorder, 82 patients with bipolar disorder, and 81 healthy controls were assessed. Clinical diagnosis was performed by psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Participants were instructed to fix their eyes on a cross on the monitor during the collection of resting-state electroencephalograph signals.ResultsA …

Authors

Yu Lei,Abdelkader Nasreddine Belkacem,Xiaotian Wang,Sha Sha,Changming Wang,Chao Chen

Journal

Biomedical Signal Processing and Control

Published Date

2022/2/1

Brain Network Analysis of Hand Motor Execution and Imagery Based on Conditional Granger Causality

The exploration of neural activity patterns in motor imagery offers a new way of thinking for improving motor skills in normal individuals and for rehabilitating patients with motor disorders. In this paper, the influence relationship between the brain network of the brain motor system and the relevant motor intervals was investigated by collecting EEG signals during finger motor execution and motor imagery from 11 subjects. To address the problem that Granger causality can only reflect the interaction between two temporal variables, a conditional Granger causality analysis was introduced to analysis the brain network relationships between multiple motor compartments. The results showed that the brain network map of finger motor execution had more effective connections than that of finger motor imagination, and it was found that there were effective connection loops between left PMA and left MA, left MA and left SA …

Authors

Yuqing He,Bin Hao,Abdelkader Nasreddine Belkacem,Jiaxin Zhang,Penghai Li,Jun Liang,Changming Wang,Chao Chen

Published Date

2022/7/23

Brain-computer interface approach for improving the pedagogical practices for virtual learning: A conceptual framework

The coronavirus epidemic (COVID19) has com-pelled the global halting of various services, including educational service, resulting in a massive crisis-response movement of education institutions to online learning platforms. Therefore, teachers had to shift from the traditional face-to-face modality and quickly adapt to virtual learning to continue their education. This conceptual paper discusses a theoretical framework for mon-itoring and improving the level of interaction between students and teachers during virtual learning environments. Through this interaction, teachers can gather some essential cognitive learning behaviors of their students by collecting some biomedical signals. In this conceptual framework, we propose a theoretical end-to-end approach to support teachers in understanding the cognitive learning behaviors of their students during online learning and where face-to-face contact is not possible …

Authors

Nuraini Jamil,Abdelkader Nasreddine Belkacem,Elhadj Benkhelifa

Published Date

2022/9/29

Electroencephalography-Neurofeedback for Decoding and Modulating Human Emotions

Emotions play an important role in the health and well-being of humans. It is associated with feedback on human interaction with the surrounding environment, decision-making, and intelligence. Electroencephalography (EEG)-based brain-computer interfaces (BCI) technology can be used to sense the emotional state of humans. Therefore, this research introduces a non-invasive BCI system that provides solutions for psychiatrists to treat patients suffering from chronic sadness, depression, and anxiety without medications. Here, we propose an EEG-based neurofeedback system for decoding and modulating human emotions. This system decodes three emotions: happiness, sadness, and neutral emotions. From the decoded emotion, the system generates visual and auditory feedback to train the patient to regulate his/her brain activity to improve his/her mental health. We collected EEG data corresponding to each …

Authors

Sara Mohammed Alzahmi,Bashayer Mohammed Alyammahi,Maitha Saeed Alyammahi,Mariam Rashed Alshamsi,Abdelkader Nasreddine Belkacem

Published Date

2022/12/6

Acknowledgment to Reviewers of Sensors in 2021

Rigorous peer-reviews are the basis of high-quality academic publishing. Thanks to the great efforts of our reviewers, Sensors was able to maintain its standards for the high quality of its published papers. Thanks to the contribution of our reviewers, in 2021, the median time to first decision was 16 days and the median time to publication was 40 days. The editors would like to extend their gratitude and recognition to the following reviewers for their precious time and dedication, regardless of whether the papers they reviewed were finally published:

Authors

Sensors Editorial Office

Published Date

2022/1/29

Classification of EEG signals based on GA-ELM optimization algorithm

There are many unpredictable problems in motion visualization and observation in BCI system, such as interference from external noise and visual fatigue of subjects. These problems seriously affect the performance of the whole BCI system. To solve this problem, this paper designed the experimental paradigm of imagination and observation, and built the eeg acquisition platform by combining UNITY and MATLAB. Ten healthy subjects participated in the experiment, which was divided into two stages: in the first stage, each subject was required to perform five experiments at the same time. In the second stage, after an interval of more than one month, the eeg signals of the 10 subjects were collected again (the same experimental paradigm). In pattern recognition and Hilbert huang transform time and frequency domain characteristics of extreme learning machine recognition classification based on genetic algorithm …

Authors

Weiguo Zhang,Lin Lu,Abdelkader Nasreddine Belkacem,Jiaxin Zhang,Penghai Li,Jun Liang,Changming Wang,Chao Chen

Published Date

2022/7/23

An improved-Multi-Input Deep Convolutional Neural Network for Automatic Emotion Recognition

Current decoding algorithms based on a one-dimensional (1D) convolutional neural network (CNN) have shown effectiveness in the automatic recognition of emotional tasks using physiological signals. However, these recognition models usually take a single modal of physiological signal as input, and the inter-correlates between different modalities of physiological signals are completely ignored, which could be an important source of information for emotion recognition. Therefore, a complete end-to-end multi-input deep convolutional neural network (MI-DCNN) structure was designed in this study. The newly designed 1D-CNN structure can take full advantage of multi-modal physiological signals and automatically complete the process from feature extraction to emotion classification simultaneously. To evaluate the effectiveness of the proposed model, we designed an emotion elicitation experiment and collected a total of 52 participants' physiological signals including electrocardiography (ECG), electrodermal activity (EDA), and respiratory activity (RSP) while watching emotion elicitation videos. Subsequently, traditional machine learning methods were applied as baseline comparisons; for arousal, the baseline accuracy and f1-score of our dataset were 62.9 ± 0.9% and 0.628 ± 0.01, respectively; for valence, the baseline accuracy and f1-score of our dataset were 60.3 ± 0.8% and 0.600 ± 0.01, respectively. Differences between the MI-DCNN and single-input DCNN were also compared, and the proposed method was verified on two public datasets (DEAP and DREAMER) as well as our dataset. The computing results in our dataset showed a …

Authors

Peiji Chen,Bochao Zou,Abdelkader Nasreddine Belkacem,Xiangwen Lyu,Xixi Zhao,Weibo Yi,Zhaoyang Huang,Jun Liang,Chao Chen

Journal

Frontiers in Neuroscience

Published Date

2022/9

Brain Network Analysis Results of Finger Motor Execution and Motor Imagery

In recent years, finger movement execution and motor imagery have become a new method in the field of motor function rehabilitation after stroke, which has important reference value for the rehabilitation of patients with motor dysfunction. The ability of motor imagery retained by stroke patients makes it possible to recover the function of neural plasticity motor system. In this paper, conditional Granger causality analysis method is used. On the premise that the residuals of the subjects meet the Durbin White test criterion (P>0.60), five network nodes directly related to motor function are selected to analyze and compare the conditional Granger causality connectivity and connection strength between these regions. Drawing the whole brain network diagram of left-hand finger movement execution and movement imagination, getting the connection direction and connection strength of each brain network node, looking for …

Authors

Fengyue Liu,Lin Lu,Abdelkader Nasreddine Belkacem,Jiaxin Zhang,Penghai Li,Jun Liang,Changming Wang,Chao Chen

Published Date

2022/11/17

An Improved Method for Removing the Artifacts of Electrooculography

In order to solve the problem that the removal effect of EEG artifacts is not ideal in EEG preprocessing, PCA-JADE-ARX is used in this paper. Firstly, PCA is used to select the number of components, and then JADE method is used to remove the artifacts. Based on the results of removing the artifacts, ARX model is estimated to find the optimal model and complete the correction of JADE results. Finally, clean and reliable real EEG signals are restored. The relative error, stability and effectiveness of the method are improved, which shows the practical application of the method.

Authors

Huimin Zhao,Chao Chen,Abdelkader Nasreddine Belkacem,Jiaxin Zhang,Lin Lu,Penghai Li

Published Date

2022

Optimized Multiscale Entropy Model Based on Resting-State fMRI for Appraising Cognitive Performance in Healthy Elderly

Many studies have indicated that an entropy model can capture the dynamic characteristics of resting-state functional magnetic resonance imaging (rfMRI) signals. However, there are problems of subjectivity and lack of uniform standards in the selection of model parameters relying on experience when using the entropy model to analyze rfMRI. To address this issue, an optimized multiscale entropy (MSE) model was proposed to confirm the parameters objectively. All healthy elderly volunteers were divided into two groups, namely, excellent and poor, by the scores estimated through traditional scale tests before the rfMRI scan. The parameters of the MSE model were optimized with the help of sensitivity parameters such as receiver operating characteristic (ROC) and area under the ROC curve (AUC) in a comparison study between the two groups. The brain regions with significant differences in entropy values were considered biomarkers. Their entropy values were regarded as feature vectors to use as input for the probabilistic neural network in the classification of cognitive scores. Classification accuracy of 80.05% was obtained using machine learning. These results show that the optimized MSE model can accurately select the brain regions sensitive to cognitive performance and objectively select fixed parameters for MSE. This work was expected to provide the basis for entropy to test the cognitive scores of the healthy elderly.

Authors

Fan Yang,Fuyi Zhang,Abdelkader Nasreddine Belkacem,Chong Xie,Ying Wang,Shenghua Chen,Zekun Yang,Zibo Song,Manling Ge,Chao Chen

Journal

Computational and Mathematical Methods in Medicine

Published Date

2022/6/7

Utilization of passive visual perception task in detecting patients with major depressive disorder for active health

Depression is a common emotional and mental disease. At present, doctors' diagnosis mainly depends on the existing evaluation scales and their accumulated experience, lack of objective electrophysiological quantitative evaluation indicators. This study explores the difference in event-related potential (ERP) between patients with depression and healthy controls under the stimulation of multi-dimensional tasks, extracts the characteristic data, and uses a t-test for statistical analysis to provide an objective evaluation index for the clinical diagnosis of depression. Ninety-nine patients in the major depression group (MDD) and thirty patients in the healthy control group (HC) were used to compare the responses to positive, negative, and neutral stimulation, the results showed that there were significant differences between the left and right occipital lobes and one frontal lobe, and the frontal lobe showed lateralization …

Authors

Chao Chen,Xin Wang,Abdelkader Nasreddine Belkacem,Sha Sha,Xixi Zhao,Changming Wang

Journal

Methods

Published Date

2022/9/1

The Classification Method of EEG Motor Imagery Based on INFO-LSSVM

For the current situation that the classification accuracy of EEG motor image data is not high in the BCI system, a vector weighted average algorithm optimization algorithm is proposed, and the optimized least squares support vector machine algorithm is proposed to classify the EEG motor image data. A motor imagination EEG experimental paradigm was designed and compared with the unoptimized LSSVM and three other typical classification methods on the same dataset. The experimental data were band-pass filtered by the fourth-order Butterworth filter of 0.5-30Hz, and the electrical interference was removed by independent component analysis. The HHT features obtained by empirical mode decomposition (EMD) and Hilbert Yellow transform (HHT) in the time-frequency domain were input into INFO-LSSVM for classification. Compared with dense feature fusion convolutional neural network (DFFN), Restricted …

Authors

Xinrong Wang,Abdelkader Nasreddine Belkacem,Penghai Li,Zufeng Zhang,Jun Liang,Dongdong Du,Chao Chen

Published Date

2022/11/17

WPT-enabled UAV trajectory design for Healthcare delivery using reinforcement learning

Over the last few years, the use of unmanned aerial vehicles (UAVs) has grown, with the goal of being widely deployed in sectors such as deliveries, rescue operations, mining fields, patrolling, and monitoring. However, the limitations of the onboard battery capacity and the flying range pose a problem to most applications while performing daily tasks such as parcel delivery or aerial communications in large areas. This paper proposes a reinforcement learning method to compute optimal trajectories for a UAV, considering both visiting delivery locations and recharging stations. The use of wireless power transfer (WPT) technology allows UAV s to wirelessly recharge their batteries on the fly and therefore to extend their flying range further. In this scenario, we consider several WPT-enabled charging stations placed around the serviced area. The proposed approach leverages a reinforcement learning strategy, and the …

Authors

Adel Merabet,Abderrahmane Lakas,Abdelkader Nasreddine Belkacem

Published Date

2022/5/30

Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI

ObjectiveThe conventional single-person brain–computer interface (BCI) systems have some intrinsic deficiencies such as low signal-to-noise ratio, distinct individual differences, and volatile experimental effect. To solve these problems, a centralized steady-state visually evoked potential collaborative BCI system (SSVEP-cBCI), which characterizes multi-person electroencephalography (EEG) feature fusion was constructed in this paper. Furthermore, three different feature fusion methods compatible with this new system were developed and applied to EEG classification, and a comparative analysis of their classification accuracy was performed with transfer learning-based convolutional neural network (TL-CNN) approach.ApproachAn EEG-based SSVEP-cBCI system was set up to merge different individuals’ EEG features stimulated by the instructions for the same task, and three feature fusion methods were adopted, namely parallel connection, serial connection, and multi-person averaging. The fused features were then input into CNN for classification. Additionally, transfer learning (TL) was applied first to a Tsinghua University (THU) benchmark dataset, and then to a collected dataset, so as to meet the CNN training requirement with a much smaller size of collected dataset and increase the classification accuracy. Ten subjects were recruited for data collection, and both datasets were used to gauge the three fusion algorithms’ performance.Main resultsThe results predicted by TL-CNN approach in single-person mode and in multi-person mode with the three feature fusion methods were compared. The experimental results show that each multi …

Authors

Penghai Li,Jianxian Su,Abdelkader Nasreddine Belkacem,Longlong Cheng,Chao Chen

Journal

Frontiers in Neuroscience

Published Date

2022/8

Comparative Study on EEG Feature Recognition based on Deep Belief Network

In Brain Computer interface (BCI) system, motor imagination has some problems, such as difficulty in extracting EEG signal features, low accuracy of classification and recognition, long training time and gradient saturation in feature classification based on traditional deep neural network, etc. In this paper, a deep belief network (DBN) model is proposed. Fast Fourier transform (FFT) and wavelet transform (WT) combined with deep machine learning model DBN were used to extract the feature vectors of time-frequency signals of different leads, superposition and average them, and then perform classification experiments. The number of DBN network layers and the number of neurons in each layer were determined by iteration. Through the reverse fine-tuning, the optimal weight coefficient W and the paranoid term B are determined layer by layer, and the training and optimization problems of deep neural networks are …

Authors

Guangrong Liu,Bin Hao,Abdelkader Nasreddine Belkacem,Jiaxin Zhang,Penghai Li,Jun Liang,Changming Wang,Chao Chen

Published Date

2022/11/17

Secure Password Using EEG-based BrainPrint System: Unlock Smartphone Password Using Brain-Computer Interface Technology

As security becomes a strong factor in daily activities, finding secure ways to unlock machines and smartphones is a challenge due to hardware limitations and the high risk of hacking. Considering the level of security and privacy in the digital world, attackers tend to be one step ahead. Therefore, this technical paper introduces a brain-computer interface (BCI) for increasing subject-based security using unique biometric features as a solution to build complex passwords. The BCI measures brain changes and extracts relevant bio-features from each subject using non-invasive electroencephalogram (EEG) tests. The proposed system allows users to gain access to their devices using brain waves (bypass) instead of inserting their password manually (normal path), which saves the user time and upgrades the level of privacy as no physical actions are required during this process. This system is also well suited for …

Authors

Zuwaina Alkhyeli,Ayesha Alshehhi,Mazna Alhemeiri,Salma Aldhanhani,Khalil AlBalushi,Fatima Ali AlNuaimi,Abdelkader Nasreddine Belkacem

Published Date

2022/12/6

Interictal Spike and Loss of Hippocampal Theta Rhythm Recorded by Deep Brain Electrodes during Epileptogenesis

Epileptogenesis is the gradual dynamic process that progressively led to epilepsy, going through the latent stage to the chronic stage. During epileptogenesis, how the abnormal discharges make theta rhythm loss in the deep brain remains not clear. In this paper, a loss of theta rhythm was estimated based on time–frequency power using the longitudinal electroencephalography (EEG), recorded by deep brain electrodes (e.g., the intracortical microelectrodes such as stereo-EEG electrodes) with monitored epileptic spikes in a rat from the first region in the hippocampal circuit. Deep-brain EEG was collected from the period between adjacent sporadic interictal spikes (lasting 3.56 s—35.38 s) to the recovery period without spikes by videos while the rats were performing exploration. We found that loss of theta rhythm became more serious during the period between adjacent interictal spikes than during the recovery period without spike, and during epileptogenesis, more loss was observed at the acute stage than the chronic stage. We concluded that the emergence of the interictal spike was the direct cause of loss of theta rhythm, and the inhibitory effect of the interictal spike on ongoing theta rhythm was persistent as well as time dependent during epileptogenesis. With the help of the intracortical microelectrodes, this study provides a temporary proof of interictal spikes to produce ongoing theta rhythm loss, suggesting that the interictal spikes could correlate with the epileptogenesis process, display a time-dependent feature, and might be a potential biomarker to evaluate the deficits in theta-related memory in the brain.

Authors

Xiaoxuan Fu,Youhua Wang,Abdelkader Nasreddine Belkacem,Yingxin Cao,Hao Cheng,Xiaohu Zhao,Shenghua Chen,Chao Chen

Journal

Sensors

Published Date

2022/2/1

Prediction of balance function for stroke based on EEG and fNIRS features during ankle dorsiflexion

Balance rehabilitation is exceedingly crucial during stroke rehabilitation and is highly related to the stroke patients’ secondary injuries (caused by falling). Stroke patients focus on walking ability rehabilitation during the early stage. Ankle dorsiflexion can activate the brain areas of stroke patients, similar to walking. The combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) was a new method, providing more beneficial information. We extracted the event-related desynchronization (ERD), oxygenated hemoglobin (HBO), and Phase Synchronization Index (PSI) features during ankle dorsiflexion from EEG and fNIRS. Moreover, we established a linear regression model to predict Berg Balance Scale (BBS) values and used an eightfold cross validation to test the model. The results showed that ERD, HBO, PSI, and age were critical biomarkers in predicting BBS. ERD and HBO during ankle dorsiflexion and age were promising biomarkers for stroke motor recovery.

Authors

Jun Liang,Yanxin Song,Abdelkader Nasreddine Belkacem,Fengmin Li,Shizhong Liu,Xiaona Chen,Xinrui Wang,Yueyun Wang,Chunxiao Wan

Journal

Frontiers in Neuroscience

Published Date

2022/8

Brain Patterns During Single- and Dual-Task Leg Movements

The brain is able to engage in dual tasks such as motor imagery (MI) and action observation (AO) or motor execution (ME) with action observation. In this study, we have quantitatively compared event-related desynchronization (ERD) patterns during tasks of pure MI, MI with AO (O-MI), ME, and ME with AO (O-ME) of the leg to investigate the underlying neuronal mechanisms using EEG. Subjects were instructed to imagine or perform rhythmical actions while watching a video of leg movements during O-MI and O-ME tasks; In contrast, subjects imagined and performed the leg movements without observing any video during pure MI and ME tasks. We noticed that the amplitude of ERDs from MI, O-MI, ME and O-ME sequentially increases in central regions of the brain. These quantified ERD patterns in EEG were used to study the differences of brain oscillatory changes among the four tasks. We found that ERDs in motor …

Authors

Penghai Li,Han Xu,Abdelkader Nasreddine Belkacem,Jianfeng Zhang,Rui Xu,Xinpu Guo,Xiaotian Wang,Dongyue Wu,Wenjun Tan,Duk Shin,Jun Liang,Chao Chen

Journal

Journal of Medical Imaging and Health Informatics

Published Date

2021

A cooperative EEG-based BCI control system for robot–drone interaction

Brain–computer interfaces (BCIs) are an emerging technology with applications for persons with disabilities as well as the able-bodied. In this paper, we present a new framework of cooperative BCI control system for robot–drone interaction using P300-based BCI. This system is aimed at supporting and assisting complex and cooperative multitask military applications. In our online experiments, a robot “BB-8” and a parrot drone are separately mind-controlled to execute cooperative tasks using noninvasive brain measurements. We use real-time electroen-cephalography (EEG) signals to drive cooperative mission-based tasks by exchanging control information between two BCI users. The proposed cooperative BCI system is based on controlling the mobile robot and drone using the P300 speller modality and exchanging mapped messages between these two wearable EEG-headset-based systems. Using the EEG …

Authors

Abdelkader Nasreddine Belkacem,Abderrahmane Lakas

Published Date

2021/6/28

Video-based physiological measurement using 3d central difference convolution attention network

Remote photoplethysmography (rPPG) is a non-contact method to measure physiological signals, such as heart rate (HR) and respiratory rate (RR), from facial videos. In this paper, we constructed a central difference convolutional attention network with Huber loss to perform more robust remote physiological signal measurements. The proposed method consists of two key parts:1) Using central difference convolution to enhance the spatiotemporal representation, which can capture rich physiological related temporal context by gathering time difference information 2) Using Huber loss as the loss function, the gradient can be smoothly reduced as the loss value between the rPPG and ground truth PPG signal is closer to the minimum. Through experiments on multiple public datasets and cross-dataset evaluation, the good performance and robustness of the rPPG measurement network based on central difference …

Authors

Yu Zhao,Bochao Zou,Fan Yang,Lin Lu,Abdelkader Nasreddine Belkacem,Chao Chen

Published Date

2021/8/4

Integrating Optimized Multiscale Entropy Model with Machine Learning for the Localization of Epileptogenic Hemisphere in Temporal Lobe Epilepsy Using Resting-State fMRI

The bottleneck associated with the validation of the parameters of the entropy model has limited the application of this model to modern functional imaging technologies such as the resting-state functional magnetic resonance imaging (rfMRI). In this study, an optimization algorithm that could choose the parameters of the multiscale entropy (MSE) model was developed, while the optimized effectiveness for localizing the epileptogenic hemisphere was validated through the classification rate with a supervised machine learning method. The rfMRI data of 20 mesial temporal lobe epilepsy patients with positive indicators (the indicators of epileptogenic hemisphere in clinic) in the hippocampal formation on either left or right hemisphere (equally divided into two groups) on the structural MRI were collected and preprocessed. Then, three parameters in the MSE model were statistically optimized by both receiver operating characteristic (ROC) curve and the area under the ROC curve value in the sensitivity analysis, and the intergroup significance of optimized entropy values was utilized to confirm the biomarked brain areas sensitive to the epileptogenic hemisphere. Finally, the optimized entropy values of these biomarked brain areas were regarded as the feature vectors input for a support vector machine to classify the epileptogenic hemisphere, and the classification effectiveness was cross-validated. Nine biomarked brain areas were confirmed by the optimized entropy values, including medial superior frontal gyrus and superior parietal gyrus ( < .01). The mean classification accuracy was greater than 90%. It can be concluded that combination of the …

Authors

Xiaoxuan Fu,Youhua Wang,Abdelkader Nasreddine Belkacem,Qirui Zhang,Chong Xie,Yingxin Cao,Hao Cheng,Shenghua Chen

Journal

Journal of Healthcare Engineering

Published Date

2021/10

Wearable eye tracking system

There is provided a method and wearable eye-tracking device for determining a fatigue level of a user, the method comprising the steps of acquiring two channels of an observed EEG (electro-encephalogram) signal using a plurality of silver chloride (AgCl) electrodes positioned in contact with and around the user's ear, obtaining user's inputs for a plurality of psychological questions and calculating an evaluation metric, decomposing the observed EEG signal using filter and blind signal separation techniques into a plurality of features, classifying and converting the plurality of features in combination with the calculated evaluation metric to a fatigue level using a classification algorithm and fuzzy logic and outputting the obtained fatigue level along with customized prompts to the user through visual and audio signals for preventing an accident.

Published Date

2021/6/15

An Optimal Smooth Model based EEG Analysis Method

Aiming at the problem of unsatisfactory noise removal effect during the preprocessing of MI EEG signals, an optimal smooth model based on empirical mode decomposition is introduced. First, use empirical mode decomposition (EMD) to decompose the complex signal into a certain number of intrinsic mode functions (IMFs) and a remainder function (r), and then construct a smooth model function based on the mean square error and smoothing index, using different The function of the intrinsic mode is combined with the optimal smooth model function to reduce noise and obtain the optimal solution. The research results show that compared with several denoising methods commonly used in other literature, this method has improved SNR, RMSE, and PE three evaluation indicators. The proposed method can provide a reference for the preprocessing of motor imagery signals.

Authors

Chao Chen,Tianxu Shang,Abdelkader Nasreddine Belkacem,Shanting Zhang,Lin Lu,Penghai Li

Published Date

2021/7/26

Research Article Integrating Optimized Multiscale Entropy Model with Machine Learning for the Localization of Epileptogenic Hemisphere in Temporal Lobe Epilepsy Using Resting …

Research Article Integrating Optimized Multiscale Entropy Model with Machine Learning for the Localization of Epileptogenic Hemi Page 1 Research Article Integrating Optimized Multiscale Entropy Model with Machine Learning for the Localization of Epileptogenic Hemisphere in Temporal Lobe Epilepsy Using Resting-State fMRI Xiaoxuan Fu,1,2 Youhua Wang,1,2 Abdelkader Nasreddine Belkacem ,3 Qirui Zhang,4 Chong Xie,1,2 Yingxin Cao,1,2 Hao Cheng,1,2 and Shenghua Chen 1,2 1State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China 2Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin 300130, China 3Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, UAE …

Authors

Xiaoxuan Fu,Youhua Wang,Abdelkader Nasreddine Belkacem,Qirui Zhang,Chong Xie,Yingxin Cao,Hao Cheng,Shenghua Chen

Published Date

2021

Abnormal Respiratory Sounds Classification using Deep CNN through Artificial Noise Addition

Respiratory sound (RS) attributes and their analyses structure a fundamental piece of pneumonic pathology, and it gives symptomatic data regarding a patient's lung. A couple of decades back, doctors depended on their hearing to distinguish symptomatic signs in lung audios by utilizing the typical stethoscope, which is usually considered a cheap and secure method for examining the patients. Lung disease is the third most ordinary cause of death worldwide, so; it is essential to classify the RS abnormality accurately to overcome the death rate. In this research, we have applied Fourier analysis for the visual inspection of abnormal respiratory sounds. Spectrum analysis was done through Artificial Noise Addition (ANA) in conjunction with different deep convolutional neural networks (CNN) to classify the seven abnormal respiratory sounds—both continuous (CAS) and discontinuous (DAS). The proposed framework contains an adaptive mechanism of adding a similar type of noise to unhealthy respiratory sounds. ANA makes sound features enough reach to be identified more accurately than the respiratory sounds without ANA. The obtained results using the proposed framework are superior to previous techniques since we simultaneously considered the seven different abnormal respiratory sound classes.

Authors

Rizwana Zulfiqar,Fiaz Majeed,Rizwana Irfan,Hafiz Tayyab Rauf,Elhadj Benkhelifa,Abdelkader Nasreddine Belkacem

Journal

Frontiers in Medicine

Published Date

2021/10

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain–Computer Interfaces: A Systematic Literature Review

Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number …

Authors

Nuraini Jamil,Abdelkader Nasreddine Belkacem,Sofia Ouhbi,Abderrahmane Lakas

Published Date

2021/7/12

Vehicles detection based on their seismic surface waves using classification techniques

Current vehicle classification techniques have depended on the information of photos or videos. However, these techniques usually face criticism because the techniques invade drivers' privacy and expose their identity. Also, the vehicle identification based on seismic ambient noise signals was difficult because of various noises. In this paper, we consider the time series data similarities between vehicles based on the seismic signal. We have applied different Artificial Intelligence (AI) techniques to construct the training model using raw data of three different sizes of vehicles (bus/truck, car, motorcycle) and surrounding noise. This study investigates the decoding performance of Logistic Regression (LR), Support Vector Machine (SVM), and Naïve Bayes (NB) to classify vehicles by using vertical component data. The 2-class classifiers were trained on 600s and tested on 100s for each class. The Naïve Bayes has …

Authors

Ahmad Bahaa Ahmad,Takeshi Tsuji,Hakim Saibi,Abdelkader Nasreddine Belkacem

Published Date

2021/12/24

EEG-based anxious states classification using affective BCI-based closed neurofeedback system

Purpose Anxiety disorder is one of the psychiatric disorders that involves extreme fear or worry, which can change the balance of chemicals in the brain. To the best of our knowledge, the evaluation of anxiety state is still based on some subjective questionnaires and there is no objective standard assessment yet. Unlike other methods, our approach focuses on study the neural changes to identify and classify the anxiety state using electroencephalography (EEG) signals. Methods We designed a closed neurofeedback experiment that contains three experimental stages to adjust subjects’ mental state. The EEG resting state signal was recorded from thirty-four subjects in the first and third stages while EEG-based mindfulness recording was recorded in the second stage. At the end of each stage, the subjects were asked to fill a Visual Analogue Scale (VAS …

Authors

Chao Chen,Xuecong Yu,Abdelkader Nasreddine Belkacem,Lin Lu,Penghai Li,Zufeng Zhang,Xiaotian Wang,Wenjun Tan,Qiang Gao,Duk Shin,Changming Wang,Sha Sha,Xixi Zhao,Dong Ming

Journal

Journal of medical and biological engineering

Published Date

2021/4

Cognitive and affective brain–computer interfaces for improving learning strategies and enhancing student capabilities: A systematic literature review

Brain–computer interface (BCI) technology has the potential to positively contribute to the educational learning environment, which faces many challenges and shortcomings. Cognitive and affective BCIs can offer a deep understanding of brain mechanisms, which may improve learning strategies and increase brain-based skills. They can offer a better empirical foundation for teaching–learning methodologies, including adjusting learning content based on brain workload, measuring student interest of a topic, or even helping students focus on specific tasks. The latest findings from emerging BCI technology, neuroscience, cognitive sciences, and psychology could be used in learning and teaching strategies to improve student abilities in education. This study investigates and analyzes the research on BCI patterns and its implementation for enhancing cognitive capabilities of students. The results showed that there is …

Authors

Nuraini Jamil,Abdelkader Nasreddine Belkacem,Sofia Ouhbi,Christoph Guger

Published Date

2021/9/23

A Case Study on Teaching a Brain–Computer Interface Interdisciplinary Course to Undergraduates

The construction of an environment appropriate for information technology education is still challenging, especially in countries such as North Africa and the Middle East. Interdisciplinary courses that keep undergraduate students updated about emergent technologies are thus crucial for information technology education in these regions. Brain–computer interface (BCI) is a promising method that combines contemporary science, emerging technologies, and neuroeducation to establish a scientific grounding for teaching and learning. However, teaching multidisciplinary courses to undergraduates demands a combined learning approach that is challenging. Students must engage in active learning, contribute skilled participation, and imbibe additional knowledge as well as skills from traditional-type lectures. Further, they must also comprehend brain functions and use new measurement methods, advanced …

Authors

Abdelkader Nasreddine Belkacem,Abderrahmane Lakas

Published Date

2021/6/6

A Cloud-based Brain-controlled Wheelchair with Autonomous Indoor Navigation System

Paralysis is the most inhibiting among all the severe motor disabilities. Indeed, people are inflicted with paralysis as the result of an accident or a medical condition that affects - completely or partially, the way muscles and nerves function. However, these patients are cognitively aware, and their mental abilities are unimpaired, and can still be autonomous and more useful in many other ways than many able-bodied people. Brain-computer interface (BCI) technology is now being incorporated into the treatment of physically impaired patients offering them an improved mobility and thus autonomy. In this paper, we propose to develop a smart brain-controlled wheelchair with autonomous navigation system for people with severely impaired motor functions. Our proposed solution allows its users to move around in indoor premises with great flexibility and minimum instructions. That is, high-level commands such as “Go to …

Authors

Abderrahmane Lakas,Fekri Kharbash,Abdelkader Nasreddine Belkacem

Published Date

2021/6/28

Automatic Sleep Spindle Detection and Analysis in Patients with Sleep Disorders

Nowadays, Sleep disorder is a common disease, and spindle spindles are important features of the second stage non-rapid eye movement (NREM) sleep. In this paper, we propose an improved automatic detection method of spindles based on wavelet transform. The spindles automatic detector is mainly composed of wavelet transform and clustering. We collected the electroencephalography (EEG) signals of six patients with sleep disorders all night for ten hours, and then preprocessed the data and other operations, and then used our improved method to detect the sleep EEG signals by spindles. By comparing with the previous automatic detection method not improved and another automatic detection method, the results show that the accuracy of sleep spindles detection can be effectively improved. The accuracy of the improved detector is 5.19% higher than before, and 9.7% higher than that of another …

Authors

Chao Chen,Xuequan Zhu,Abdelkader Nasreddine Belkacem,Lin Lu,Long Hao,Jia You,Duk Shin,Wenjun Tan,Zhaoyang Huang,Dong Ming

Published Date

2021

EEG Classification-based Comparison Study of Motor-Imagery Brain-Computer Interface

For developing brain computer interface (BCI) applications, electroencephalography (EEG) is the most widely used measurement method due to its noninvasiveness, high temporal resolution, and portability. EEG signal contains sufficient neural information about each human task, which makes the extracting, and decoding of each task-related information is still challenging, especially to improve the existing BCI performances. In this paper, we present a comparison analysis to find the most relevant features and the most suitable classification method for decoding motor imagery for EEG-based BCI. Therefore, some signal processing and machine learning techniques have applied for features extraction and classification phases. For the decomposition of EEG signal, we used three type of features [EEG signal mean, root mean square (RMS) and Relative of band power (RBP)]. In addition, we investigated an …

Authors

Kheira Djelloul,Abdelkader Nasreddine Belkacem

Published Date

2021/9/21

Motor Imagination of Lower Limb Movements at Different Frequencies

Motor imagination (MI) is the mental process of only imagining an action without an actual movement. Research on MI has made significant progress in feature information detection and machine learning decoding algorithms, but there are still problems, such as a low overall recognition rate and large differences in individual execution effects, which make the development of MI run into a bottleneck. Aiming at solving this bottleneck problem, the current study optimized the quality of the MI original signal by “enhancing the difficulty of imagination tasks,” conducted the qualitative and quantitative analyses of EEG rhythm characteristics, and used quantitative indicators, such as ERD mean value and recognition rate. Research on the comparative analysis of the lower limb MI of different tasks, namely, high-frequency motor imagination (HFMI) and low-frequency motor imagination (LFMI), was conducted. e results validate the following: the average ERD of HFMI (− 1.827) is less than that of LFMI (− 1.3487) in the alpha band, so did (− 3.4756<− 2.2891) in the beta band. In the alpha and beta characteristic frequency bands, the average ERD of HFMI is smaller than that of LFMI, and the ERD values of the two are significantly different (p 0. 0074< 0. 01; r 0.945). e ERD intensity STD values of HFMI are less than those of LFMI. which suggests that the ERD intensity individual difference among the subjects is smaller in the HFMI mode than in the LFMI mode. e average recognition rate of HFMI is higher than that of LFMI (87.84%> 76.46%), and the recognition rate of the two modes is significantly different (p 0. 0034< 0. 01; r 0.429). In summary, this …

Authors

Yingtao Liu,Chao Chen,Abdelkader Nasreddine Belkacem,Zhiyong Wang,Longlong Cheng,Chun Wang,Yuexiao Chang,Penghai Li

Journal

Journal of Healthcare Engineering

Published Date

2021/12/22

A simulation on relation between power distribution of low-frequency field potentials and conducting direction of rhythm generator flowing through 3d asymmetrical brain tissue

Although the power of low-frequency oscillatory field potentials (FP) has been extensively applied previously, few studies have investigated the influence of conducting direction of deep-brain rhythm generator on the power distribution of low-frequency oscillatory FPs on the head surface. To address this issue, a simulation was designed based on the principle of electroencephalogram (EEG) generation of equivalent dipole current in deep brain, where a single oscillatory dipole current represented the rhythm generator, the dipole moment for the rhythm generator’s conducting direction (which was orthogonal and rotating every 30 degrees and at pointing to or parallel to the frontal lobe surface) and the (an)isotropic conduction medium for the 3D (a)symmetrical brain tissue. Both the power above average (significant power value, SP value) and its space (SP area) of low-frequency oscillatory FPs were employed to respectively evaluate the strength and the space of the influence. The computation was conducted using the finite element method (FEM) and Hilbert transform. The finding was that either the SP value or the SP area could be reduced or extended, depending on the conducting direction of deep-brain rhythm generator flowing in the (an)isotropic medium, suggesting that the 3D (a)symmetrical brain tissue could decay or strengthen the spatial spread of a rhythm generator conducting in a different direction.

Authors

Hao Cheng,Manling Ge,Abdelkader Nasreddine Belkacem,Xiaoxuan Fu,Chong Xie,Zibo Song,Shenghua Chen,Chao Chen

Journal

Symmetry

Published Date

2021/5/19

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework

Respiratory symptoms can be caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms: coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases, such as the COVID-19 pandemic. Among the factors that contributed to the spread of the COVID-19 pandemic were the late diagnosis or misinterpretation of COVID-19 symptoms as regular flu-like symptoms. Research has shown that one of the possible differentiators of the underlying causes of different respiratory diseases could be the cough sound, which comes in different types and forms. A reliable lab-free tool for early and accurate diagnosis, which can differentiate between different respiratory diseases is therefore very much needed, particularly during the current pandemic. This concept paper discusses a medical hypothesis of an end-to-end portable system that can record data from patients with symptoms, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly theoretical solution could play an important part in the early diagnosis.

Authors

Abdelkader Nasreddine Belkacem,Sofia Ouhbi,Abderrahmane Lakas,Elhadj Benkhelifa,Chao Chen

Journal

Frontiers in Medicine

Published Date

2021/3/31

Efficacy Evaluation of Neurofeedback-Based Anxiety Relief

Anxiety disorder is a mental illness that involves extreme fear or worry, which can alter the balance of chemicals in the brain. This change and evaluation of anxiety state are accompanied by a comprehensive treatment procedure. It is well-known that the treatment of anxiety is chiefly based on psychotherapy and drug therapy, and there is no objective standard evaluation. In this paper, the proposed method focuses on examining neural changes to explore the effect of mindfulness regulation in accordance with neurofeedback in patients with anxiety. We designed a closed neurofeedback experiment that includes three stages to adjust the psychological state of the subjects. A total of 34 subjects, 17 with anxiety disorder and 17 healthy, participated in this experiment. Through the three stages of the experiment, electroencephalography (EEG) resting state signal and mindfulness-based EEG signal were recorded. Power spectral density was selected as the evaluation index through the regulation of neurofeedback mindfulness, and repeated analysis of variance (ANOVA) method was used for statistical analysis. The findings of this study reveal that the proposed method has a positive effect on both types of subjects. After mindfulness adjustment, the power map exhibited an upward trend. The increase in the average power of gamma wave indicates the relief of anxiety. The enhancement of the wave power represents an improvement in the subjects’ mindfulness ability. At the same time, the results of ANOVA showed that P < 0.05, i.e., the difference was significant. From the aspect of neurophysiological signals, we objectively evaluated the ability of …

Authors

Chao Chen,Xiaolin Xiao,Abdelkader Nasreddine Belkacem,Lin Lu,Xin Wang,Weibo Yi,Penghai Li,Changming Wang,Sha Sha,Xixi Zhao,Dong Ming

Journal

Frontiers in neuroscience

Published Date

2021/10/28

A Decoding Algorithm for Non-invasive SSVEP-based Drone Flight Control

Many advanced researches on natural user interfaces methods based on user-centered design have been using speech, gestures and vision to interact with environment and/or control internet of things (IoT) devices. Brain computer interfaces (BCIs) technology could make this interaction/control more natural, faster, and reliable, and effective. In this paper, we propose a decoding algorithm for controlling a drone in a three-dimensional (3D) space using steady state visually evoked potential (SSVEP)-based BCI modality. SSVEP-based BCI has the great potential for use in virtual reality environment, which enables the user to control the drone using his/her brain activity in an first-person-view mode. Therefore, the user will be in a full control over the flight using BCI system by commanding the drone to take off, land, go forward, stop, and turn right/left. This system yields a super convenient way for normal people with no …

Authors

Abdelhadi Hireche,Yasmine Zennaia,Redouane Ayad,Abdelkader Nasreddine Belkacem

Published Date

2021/12/9

A framework for course-embedded assessment for evaluating learning outcomes of a network programming course

The assessment of course learning outcomes is an essential component in the continuous efforts of course improvement. The assessment is a tedious process and often incurs for many educators an overhead to the teaching and learning operation. Thus the need to investigate efficient methods to improve the process of course assessment by minimizing unnecessary efforts for the planning, preparation and execution of the assessment process. Automating the assessment process is instrumental in taking away its tediousness allowing teachers to focus their efforts on the improvement of the teaching and learning quality. For the case of information technology (IT) curriculum, one main concern is the difficulties encountered by students in learning programming skills; thus the need for an assessment-driven course improvement for programming courses. In this paper, we propose an automated proactive assessment …

Authors

Abderrahmane Lakas,Abdelkader Nasreddine Belkacem

Published Date

2021/4/21

EEG-controlled wall-crawling cleaning robot using SSVEP-based brain-computer interface

Research Article EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface Page 1 Research Article EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface Lei Shao,1 Longyu Zhang,1 Abdelkader Nasreddine Belkacem ,2 Yiming Zhang,1 Xiaoqi Chen,1 Ji Li ,1 and Hongli Liu1 1Key Laboratory for Control eory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China 2Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain, PO Box 15551, UAE Correspondence should be addressed to Abdelkader Nasreddine Belkacem; belkacem@uaeu.ac.ae Received 13 August 2019; Revised 29 October 2019; Accepted 29 November 2019; Published 11 January 2020 Guest Editor: Ludovico Minati Copyright © 2020 Lei Shao et al. is is an open …

Authors

Lei Shao,Longyu Zhang,Abdelkader Nasreddine Belkacem,Yiming Zhang,Xiaoqi Chen,Ji Li,Hongli Liu

Journal

Journal of healthcare engineering

Published Date

2020

A Novel Cooperative Game for Reinforcing Obesity Awareness Amongst Children in UAE

This paper presents a funny cooperative game that makes kids interact with their parents to indirectly educate both of them about the importance of making their own choices of eating unhealthy and healthy food. The game-based learning and cultivation of an informed decision-making approach throughout our proposed game design was utilized to achieve the obesity awareness objectives. This paper describes the design, implementation, and evaluation of our proposed cooperative game “ObeseGo” that was developed to enhance the obesity awareness for kids at an early age and educate them regarding their choices concerning eating a balanced meal to be able to adopt a healthy lifestyle. A survey questionnaire was conducted to evaluate our game. The results of the survey allowed us to analyze the impact and usefulness of the proposed game and to plan for improvement in future work.

Authors

Fatema Alnaqbi,Sarah Alzahmi,Ayesha Alharmoozi,Fatema Alshehhi,Muhammad Talha Zia,Sofia Ouhbi,Abdelkader Nasreddine Belkacem

Published Date

2020/5/30

Mind drone chasing using EEG-based brain computer interface

In this paper, we present a new way of controlling drone by using a P300-based brain-computer interface that supports the military field as assistive technology. The main idea is that the drone can be controlled by the soldier's brain activity using electroencephalogram (EEG) to chase other drones and discover hidden enemy areas. We assumed that we have two able-bodied users, the first role played by a user as a soldier aims to control the drone by using brain activity and the other role played by another user as an enemy aims to control manually the drone using Python program. This scenario allowed us to test the ability of chasing the enemy's drone. The results for this application was evaluated by the ability of the user to calibrate very well with the software and the ability of the program to receive and send commands using EEG signal to the drone for execution.

Authors

Fatima Ali Al-Nuaimi,Rauda Jasem Al-Nuaimi,Sara Saaed Al-Dhaheri,Sofia Ouhbi,Abdelkader Nasreddine Belkacem

Published Date

2020/7/20

Cybersecurity framework for P300-based brain computer interface

This paper describes a cybersecurity framework for protecting brain computer interface (BCI) technology. This framework consists of cybersecurity risk scenarios related to user safety/privacy and best practices to manage them. This framework provides solutions for privacy and safety issues of the existing noninvasive BCIs (e.g., electroencephalography (EEG)-based BCI). We chose to design a P300-based BCI application because it is the most popular modality, simulate some common cybersecurity attacks, and find a relevant solution to protect the user and/or integrated EEG hardware-software system. In this paper, we describe how cybersecurity risks could affect BCI form streaming/recording EEG signal in real-time until sending commands. We used EEG Equipment for measuring brain activity and Python programing language to build our experimental paradigm, record EEG signal, classify P300 components …

Authors

Abdelkader Nasreddine Belkacem

Published Date

2020/10/11

Neural processing mechanism of mental calculation based on cerebral oscillatory changes: a comparison between abacus experts and novices

Background Abacus experts can mentally perform fast mathematical operations using multi-digit numbers. The temporal dynamics of abacus mental calculations are still unknown, although some behavioural and neuroimaging studies have suggested visuospatial and visuomotor neural processes during abacus mental calculations. Therefore, this study aims to clarify the significant similarities and differences between expert and novice abacus users by investigating calculation-induced neuromagnetic responses based on cerebral oscillatory changes. Methods Twelve to 13 healthy abacus experts and 17 non-experts participated in two experimental paradigms using non-invasive neuromagnetic measurements. In both experiments 1 and 2, the spatial distributions of oscillatory changes present during mental calculations and the time and spectrotemporal profiles during addition and multiplication tasks were calculated. MEG data were analysed using synthetic aperture magnetometry (SAM) with an adaptive beamformer to calculate the group averages of the spatial distribution of oscillatory changes and their temporal frequency profiles in source level analyses. Results Using group averages of the spatial distributions of oscillatory changes, we observed some common brain activities in both right-handed abacus experts and non-experts. In the non-experts, we detected activity changes in the right dorsolateral prefrontal cortex (DLPFC) and bilateral intraparietal sulcus (IPS), whereas in experts, we mainly detected changes in the bilateral parieto-occipital sulcus (POS), right inferior frontal gyrus (IFG), and left sensorimotor area. Based on these …

Authors

Abdelkader Nasreddine Belkacem,Kanako Kiso,Etsuko Uokawa,Tetsu Goto,Shiro Yorifuji,Masayuki Hirata

Journal

Frontiers in human neuroscience

Published Date

2020/4/15

Neural activities classification of left and right finger gestures during motor execution and motor imagery

In this study, a new paradigm containing motor observation, motor execution, and motor imagery was designed to investigate whether motor imagery (MI) and motor execution (ME) of finger gestures can be used to extend commands of practical mBCIs. The subjects were instructed to perform or imagine 30 left and right finger gestures. Hierarchical support vector machine (hSVM) method was applied to classify four tasks (i.e., ME and MI tasks between left and right gestures). The average classification accuracies of motor imagery and execution tasks using fivefold cross-validation were 90.89 ± 9.87% and 74.08 ± 13.42% in first layer and second layer, respectively. The average accuracy of classification of four classes is 83.06 ± 7.29% overall. These results show that performing or imaging finger movements have the potential to extend the commands of the existing BCI, especially for healthy elderly living.

Authors

Chao Chen,Peiji Chen,Abdelkader Nasreddine Belkacem,Lin Lu,Rui Xu,Wenjun Tan,Penghai Li,Qiang Gao,Duk Shin,Changming Wang,Dong Ming

Journal

Brain-Computer Interfaces

Published Date

2021/10/2

Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients

All people experience aging, and the related physical and health changes, including changes in memory and brain function. These changes may become debilitating leading to an increase in dependence as people get older. Many external aids and tools have been developed to allow older adults and elderly patients to continue to live normal and comfortable lives. This mini-review describes some of the recent studies on cognitive decline and motor control impairment with the goal of advancing non-invasive brain computer interface (BCI) technologies to improve health and wellness of older adults and elderly patients. First, we describe the state of the art in cognitive prosthetics for psychiatric diseases. Then, we describe the state of the art of possible assistive BCI applications for controlling an exoskeleton, a wheelchair and smart home for elderly people with motor control impairments. The basic age-related brain and body changes, the effects of age on cognitive and motor abilities, and several BCI paradigms with typical tasks and outcomes are thoroughly described. We also discuss likely future trends and technologies to assist healthy older adults and elderly patients using innovative BCI applications with minimal technical oversight.

Authors

Abdelkader Nasreddine Belkacem,Nuraini Jamil,Jason A Palmer,Sofia Ouhbi,Chao Chen

Journal

Frontiers in Neuroscience

Published Date

2020/6/30

On Digital Multimedia and Human Emotions Using EEG-Based Brain Computer Interface

An expanding attention regarding human emotion is a pressing motive towards the current research in neuroscience and artificial intelligent. People need to communicate by exchanging information through verbal or nonverbal communication via sound, visual gestures (facial expression or hand/body gestures). In today’s society, digital multimedia is one of the essential elements in daily life activities that can emphasize communication and emotions adequately. People with severe motor disabilities have difficulties in communicating and showing their emotions directly. Therefore, brain computer interface (BCI) can be a helpful tool as an alternative and assistive communication tools for sharing emotional information. This paper has conducted a review analysis to present the current trend in using digital multimedia to express the human feelings for the latest five years. Twenty-nine studies were selected from IEEEXPlore, PubMed and ScienceDirect, and classified into three major categories: methodology, multimedia type and number of emotion classes. The results show the need for more case studies and games in this area. There is also a need to increase the quality and quantity of research in emotion using the electroencephalography (EEG).

Authors

Nuraini Jamil,Abderrahmane Lakas,Sofia Ouhbi,Abdelkader Nasreddine Belkacem

Published Date

2020/8/21

Research Article EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface

Research Article EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface Page 1 Research Article EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface Lei Shao,1 Longyu Zhang,1 Abdelkader Nasreddine Belkacem ,2 Yiming Zhang,1 Xiaoqi Chen,1 Ji Li ,1 and Hongli Liu1 1Key Laboratory for Control eory & Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China 2Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain, PO Box 15551, UAE Correspondence should be addressed to Abdelkader Nasreddine Belkacem; belkacem@uaeu.ac.ae Received 13 August 2019; Revised 29 October 2019; Accepted 29 November 2019; Published 11 January 2020 Guest Editor: Ludovico Minati Copyright © 2020 Lei Shao et al. is is an open …

Authors

Lei Shao,Longyu Zhang,Abdelkader Nasreddine Belkacem,Yiming Zhang,Xiaoqi Chen,Ji Li,Hongli Liu

Published Date

2020

Quadcopter robot control based on hybrid brain–computer interface system

Brain–computer interface (BCI) technology is a direct human–environment interaction system mostly based on translating brain activity into relevant commands. This technology allows direct communication between the brain and any external device that does not depend on the peripheral nervous system and muscles.(1) BCIs can improve the quality of life of patients with cerebral palsy, amyotrophic lateral sclerosis, and stroke. The brain activity of such patients can be measured invasively or noninvasively by either electrocorticography (ECoG) or electroencephalography (EEG), respectively.(2–8) The basic operation of the BCI system consists of the signal acquisition, preprocessing, feature extraction, classification, control, and feedback phases. At present, the bottleneck problems of existing single-modal BCIs are the number of tasks that cannot meet the requirements of multi-degree of freedom (DOF) control and the low rate of correct recognition task, which limits the practical application of a daily-life BCI system. To improve the performance of these BCIs, a new BCI, called a hybrid BCI (hBCI), for boosting the performance of existing single-modal BCIs in terms of accuracy and information transfer rate (ITR), has recently been proposed by many researchers (9–11) who have been trying to combine at least two BCI modalities such as P300-BCI, steady state visual evoked potential (SSVEP)-BCI, and motor-imagery-based BCI.(12–16) Other researchers tried to combine brain activity with nonbrain activity, such as eye movement activity measured using electrooculography (EOG),(17–19) muscle activity measured using electromyography (EMG),(20 …

Authors

Chao Chen,Peng Zhou,Abdelkader Nasreddine Belkacem,Lin Lu,Rui Xu,Xiaotian Wang,Wenjun Tan,Zhifeng Qiao,Penghai Li,Qiang Gao,Duk Shin

Journal

Sensors and Materials

Published Date

2020/3/15

An adaptive multi-clustered scheme for autonomous UAV swarms

Swarm technology for autonomous unmanned aerial vehicles (UAVs) has gained popularity in the last few years due to their potential for civilian and military applications. Intelligent swarm systems are very efficient at solving group-level problems and their capability to accomplish complex missions with no or little human intervention. One of the most challenging problems is operating in environments with surrounding obstacles such as buildings, thus, often obstructing inter-UAV communication. A UAV swarm is required to maintain continuous communication between its members and preserve the stability of its formation while flying towards an ultimate goal. In this paper we propose MSCS, a new cooperative and adaptive scheme for multi-clustered autonomous UAV swarms. This schemes allows several UAVs operating in a swarm formation to coordinate their navigation and path planning operations by using an …

Authors

Abderrahmane Lakas,Abdelkader Nasreddine Belkacem,Shamsa Al Hassani

Published Date

2020/6/15

See List of Professors in Abdelkader Nasreddine Belkacem University(United Arab Emirates University)

Abdelkader Nasreddine Belkacem FAQs

What is Abdelkader Nasreddine Belkacem's h-index at United Arab Emirates University?

The h-index of Abdelkader Nasreddine Belkacem has been 18 since 2020 and 18 in total.

What are Abdelkader Nasreddine Belkacem's top articles?

The articles with the titles of

Classification and transfer learning of sleep spindles based on convolutional neural networks

Increasing Accessibility to a Large Brain-Computer Interface Dataset: Curation of Physionet EEG Motor Movement/Imagery Dataset for Decoding and Classification

Face mask for accurate location of sensors relative to a users face, a communication enabling face mask and a communication system including the face mask

Convolutional Neural Network for Emotional EEG Decoding and Visualization

Decoding multi-class motor imagery and motor execution tasks using Riemannian geometry algorithms on large EEG datasets

Diagnosis of Schizophrenia from EEG signals Using ML Algorithms

On Closed-Loop Brain Stimulation Systems for Improving the Quality of Life of Patients with Neurological Disorders

On Neurodevelopmental Disorder based on Brain Computer Interface for Enhancing the Learning Process

...

are the top articles of Abdelkader Nasreddine Belkacem at United Arab Emirates University.

What are Abdelkader Nasreddine Belkacem's research interests?

The research interests of Abdelkader Nasreddine Belkacem are: Brain-computer Interface, Human-robot Interaction, Computational Neuroscience, MEG/EEG, Applied AI

What is Abdelkader Nasreddine Belkacem's total number of citations?

Abdelkader Nasreddine Belkacem has 1,196 citations in total.

What are the co-authors of Abdelkader Nasreddine Belkacem?

The co-authors of Abdelkader Nasreddine Belkacem are Hiroshi Ishiguro, Masayuki Hirata, Yasuharu Koike, Takufumi Yanagisawa, Natsue Yoshimura.

    Co-Authors

    H-index: 93
    Hiroshi Ishiguro

    Hiroshi Ishiguro

    Osaka University

    H-index: 45
    Masayuki Hirata

    Masayuki Hirata

    Osaka University

    H-index: 37
    Yasuharu Koike

    Yasuharu Koike

    Tokyo Institute of Technology

    H-index: 26
    Takufumi Yanagisawa

    Takufumi Yanagisawa

    Osaka University

    H-index: 22
    Natsue Yoshimura

    Natsue Yoshimura

    Tokyo Institute of Technology

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