Structured dictionary learning for sparse common component and innovation model

Andrew Mahyari

Abstract: Event-related potentials (ERP)s are electrophysiological responses that are commonly used for detecting the brain response to external stimuli. In this paper, we propose to use the sparse common component and innovations model (SCCI) to extract ERPs from multiple EEG signals recorded across closely located electrodes. This model finds the sparse representation of the common component of the signals and their innovation components with respect to pre-determined common and innovation dictionaries, where the common component refer to an event captured by adjacent electrodes such as ERPs. However, different stimuli may produce different responses and predetermining the dictionary may not always be optimal. Therefore, we introduce a structured dictionary learning method to simultaneously learn the two dictionaries from training data. The proposed method is applied to a study of error monitoring where two different types of brain responses are elicited corresponding to the decision made by the subject. The learned dictionaries can discriminate between the response types and extract the ERP corresponding to the two responses.