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

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.