Research Repository

Deep Learning based Prediction of EEG Motor Imagery of Stroke Patients' for Neuro-Rehabilitation Application

Raza, Haider and Chowdhury, Anirban and Bhattacharyya, Saugat (2020) Deep Learning based Prediction of EEG Motor Imagery of Stroke Patients' for Neuro-Rehabilitation Application. In: International Joint Conference on Neural Networks (IJCNN 2020), 2020-07-19 - 2020-07-24, Glasgow, UK. (In Press)

[img]
Preview
Text
WCCI_2020_EEGNet_Patient_Data.pdf - Accepted Version

Download (530kB) | Preview

Abstract

Due to the non-stationary nature of electroencephalography (EEG) signals, a Brain-computer Interfacing (BCI) system requires frequent calibration. This leads to intersession inconsistency which is one of the main reason that impedes the widespread adoption of non-invasive BCI for realworld applications, especially in rehabilitation and medicine. Domain adaptation and deep learning-based techniques have gained relevance in designing calibration-free BCIs to solve this issue. EEGNet is one such deep net architecture that has been successful in performing inter-subject classification, albeit on data from healthy participants. This is the first paper, which tests the performance of EEGNet on data obtained from 10 hemiparetic stroke patients while performing left and right motor imagery tasks. Results obtained on implementing EEGNet have been promising and it has comparably good performance as from expensive feature engineering-based approaches for both withinsubject and cross-subject classification. The less dependency on feature engineering techniques and the ability to extract generalized features for inter-subject classification makes EEGNet a promising deep-learning architecture for developing practically feasible solutions for BCI based neuro-rehabilitation applications.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: _not provided_
Uncontrolled Keywords: Deep Learning, BCI, Neuro-Rehabilitation, Machine Learning
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 23 Mar 2020 10:53
Last Modified: 23 Mar 2020 11:15
URI: http://repository.essex.ac.uk/id/eprint/27147

Actions (login required)

View Item View Item