Automatic ERP classification in EEG recordings from task-related independent components

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

Colleges, School and Institutes

Abstract

The Electroencephalography (EEG) signal contains information about a person's brain activity including the Event-Related Potential (ERP) - an evoked response to a task-related stimulus. EEG is contaminated by artefacts that degrade ERP classification performance. Independent Component Analysis (ICA) is normally employed to decompose EEG into independent components (ICs) associated to artefact and non-artefact sources. Sources identified as artefacts are removed and a cleaned EEG is reconstructed. This paper presents an alternative use of ICA for the EEG signal to extract ERP feature rather than artefact reduction. Average ERP classification accuracy increases by 15%, to 83.9%, on clinical-grade EEG data from 9 participants, when compared to similar approaches with cleaned EEG. Additionally, the proposed method obtained better performance in comparison with the state-of-the-art xDAWN method.

Details

Original languageEnglish
Title of host publicationIEEE Int. Conf. on Biomedical and Health Informatics
Publication statusPublished - 2016
Event3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 - Las Vegas, United States
Duration: 24 Feb 201627 Feb 2016

Conference

Conference3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
CountryUnited States
CityLas Vegas
Period24/02/1627/02/16