Abbas Salami

Abbas Salami

University of Essex

H-index: 4

Europe-United Kingdom

About Abbas Salami

Abbas Salami, With an exceptional h-index of 4 and a recent h-index of 4 (since 2020), a distinguished researcher at University of Essex, specializes in the field of Machine Learning, Signal Processing, Cognitive Neuroscience, Brain-Computer Interfaces, EEG.

His recent articles reflect a diverse array of research interests and contributions to the field:

Finding neural correlates of depersonalisation/derealisation disorder via explainable CNN-based analysis guided by clinical assessment scores

Explainable deep learning-based EEG analysis for biomarker discovery and its application on depersonalisation/derealisation disorder

EEG-ITNet: An explainable inception temporal convolutional network for motor imagery classification

Towards decoding of depersonalisation disorder using EEG: A time series analysis using CDTW

Symptoms of depersonalisation/derealisation disorder as measured by brain electrical activity: A systematic review

Abbas Salami Information

University

University of Essex

Position

PhD candidate

Citations(all)

74

Citations(since 2020)

74

Cited By

3

hIndex(all)

4

hIndex(since 2020)

4

i10Index(all)

3

i10Index(since 2020)

3

Email

University Profile Page

University of Essex

Abbas Salami Skills & Research Interests

Machine Learning

Signal Processing

Cognitive Neuroscience

Brain-Computer Interfaces

EEG

Top articles of Abbas Salami

Finding neural correlates of depersonalisation/derealisation disorder via explainable CNN-based analysis guided by clinical assessment scores

Authors

Abbas Salami,Javier Andreu-Perez,Helge Gillmeister

Journal

Artificial Intelligence in Medicine

Published Date

2024/3/1

Mental health disorders are typically diagnosed based on subjective reports (e.g., through questionnaires) followed by clinical interviews to evaluate the self-reported symptoms. Therefore, considering the interconnected nature of psychiatric disorders, their accurate diagnosis is a real challenge without indicators of underlying physiological dysfunction. Depersonalisation/derealisation disorder (DPD) is an example of dissociative disorder affecting 1–2 % of the population. DPD is characterised mainly by persistent disembodiment, detachment from surroundings, and feelings of emotional numbness, which can significantly impact patients' quality of life. The underlying neural correlates of DPD have been investigated for years to understand and help with a more accurate and in-time diagnosis of the disorder. However, in terms of EEG studies, which hold great importance due to their convenient and inexpensive …

Explainable deep learning-based EEG analysis for biomarker discovery and its application on depersonalisation/derealisation disorder

Authors

Abbas Salami

Published Date

2023

Mental health disorders are typically diagnosed based on subjective reports (e.g., through questionnaires) followed by clinical interviews to evaluate self-reported symptoms. Therefore, considering the interconnected nature of psychiatric disorders, their accurate diagnosis is a real challenge without indicators of underlying physiological dysfunction. Depersonalisation/derealisation disorder (DPD) is an example of dissociative disorder characterised mainly by persistent disembodiment, detachment from the surroundings, and feeling of emotional numbness. Its underlying neural correlates have been investigated to understand and help with a more accurate and in-time diagnosis of the disorder. However, in terms of EEG studies, which hold great importance due to their convenient and inexpensive nature, the literature has often been based on hypotheses proposed by experts in the field, meaning it requires prior knowledge of the disorder. In addition, participants labelling in research experiments are often derived from the outcome of the Cambridge Depersonalisation Scale (CDS), a subjective assessment to quantify the level of depersonalisation/derealisation. As a result, I aimed to propose a novel EEG processing pipeline based on deep neural networks to discover electrophysiological DPD biomarkers. My deep learning model requires no prior knowledge or assumption of the disorder. In addition, the structure of the proposed model targets the unreliability of CDS scores by using them as prior information only to guide the unsupervised learning task in a multi-task learning scenario. I have also presented new ways of network visualisation to …

EEG-ITNet: An explainable inception temporal convolutional network for motor imagery classification

Authors

Abbas Salami,Javier Andreu-Perez,Helge Gillmeister

Journal

IEEE Access

Published Date

2022/3/22

In recent years, neural networks and especially deep architectures have received substantial attention for EEG signal analysis in the field of brain-computer interfaces (BCIs). In this ongoing research area, the end-to-end models are more favoured than traditional approaches requiring signal transformation pre-classification. They can eliminate the need for prior information from experts and the extraction of handcrafted features. However, although several deep learning algorithms have been already proposed in the literature, achieving high accuracies for classifying motor movements or mental tasks, they often face a lack of interpretability and therefore are not quite favoured by the neuroscience community. The reasons behind this issue can be the high number of parameters and the sensitivity of deep neural networks to capture tiny yet unrelated discriminative features. We propose an end-to-end deep learning …

Towards decoding of depersonalisation disorder using EEG: A time series analysis using CDTW

Authors

Abbas Salami,Javier Andreu-Perez,Helge Gillmeister

Published Date

2020/12/1

Depersonalisation/derealisation refers to a transient psychological condition characterised by losing the sense of body ownership and feeling detached from the outside world. It is often accompanied by a lack of emotional responsiveness and sometimes memory fragmentation. Studies have shown the temporary occurrence of this condition among 34-70% of the general population during their life span. However, if the symptoms become consistent, they can be intolerable and can profoundly affect the quality of life in such an extent that it would be considered as one type of the dissociative disorders, depersonalisation disorder (DPD). Currently, there is no laboratory method to diagnose DPD, and studies have expressed a period of seven to 12 years for the correct diagnosis of DPD. We recently aimed to investigate DPD and its symptoms based on inexpensive and convenient electroencephalogram (EEG …

Symptoms of depersonalisation/derealisation disorder as measured by brain electrical activity: A systematic review

Authors

Abbas Salami,Javier Andreu-Perez,Helge Gillmeister

Published Date

2020/11/1

Depersonalisation/derealisation disorder (DPD) refers to frequent and persistent detachment from bodily self and disengagement from the outside world. As a dissociative disorder, DPD affects 1–2 % of the population, but takes 7–12 years on average to be accurately diagnosed. In this systematic review, we comprehensively describe research targeting the neural correlates of core DPD symptoms, covering publications between 1992 and 2020 that have used electrophysiological techniques. The aim was to investigate the diagnostic potential of these relatively inexpensive and convenient neuroimaging tools. We review the EEG power spectrum, components of the event-related potential (ERP), as well as vestibular and heartbeat evoked potentials as likely electrophysiological biomarkers to study DPD symptoms. We argue that acute anxiety- or trauma-related impairments in the integration of interoceptive and …

See List of Professors in Abbas Salami University(University of Essex)

Abbas Salami FAQs

What is Abbas Salami's h-index at University of Essex?

The h-index of Abbas Salami has been 4 since 2020 and 4 in total.

What are Abbas Salami's top articles?

The articles with the titles of

Finding neural correlates of depersonalisation/derealisation disorder via explainable CNN-based analysis guided by clinical assessment scores

Explainable deep learning-based EEG analysis for biomarker discovery and its application on depersonalisation/derealisation disorder

EEG-ITNet: An explainable inception temporal convolutional network for motor imagery classification

Towards decoding of depersonalisation disorder using EEG: A time series analysis using CDTW

Symptoms of depersonalisation/derealisation disorder as measured by brain electrical activity: A systematic review

are the top articles of Abbas Salami at University of Essex.

What are Abbas Salami's research interests?

The research interests of Abbas Salami are: Machine Learning, Signal Processing, Cognitive Neuroscience, Brain-Computer Interfaces, EEG

What is Abbas Salami's total number of citations?

Abbas Salami has 74 citations in total.

What are the co-authors of Abbas Salami?

The co-authors of Abbas Salami are Javier Andreu-Perez, Helge Gillmeister.

Co-Authors

H-index: 21
Javier Andreu-Perez

Javier Andreu-Perez

University of Essex

H-index: 18
Helge Gillmeister

Helge Gillmeister

University of Essex

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