A Phase-based EEG Epoch Selection Method for Decoding Bi-directional Hand Movement Imagination in Stroke Patients

Electroencephalogram (EEG) based non-invasive Brain Computer Interface (BCI) system is gaining significant attention as a promising solution for stroke rehabilitation. Accurate selection of informative EEG time segment, that accommodates the specific neural activity patterns associated with the underlying mental task can help to improve the efficacy of the BCI system. In this work, we propose a phase-based EEG epoch selection algorithm to extract the discriminative EEG time segment corresponding to bi-directional hand motor imagery. The imagined center-out hand movement in two directions is decoded using the selected epoch of the EEG, recorded from 16 stroke patients with hemiparesis and specifically hand weakness. Phase Lock Value (PLV) EEG features extracted from the selected EEG epoch is used as discriminative feature for binary classification of imagined hand movement direction using Linear Discriminant Analysis. The use of selected EEG epoch yielded an improvement of 11.5% and 11.7% in the average direction classification accuracy of calibration and feedback session data respectively, compared to the baseline method employing the whole EEG signal. In addition to improvement in decoding accuracy, the epoch selection also yielded an average Information Transfer Rate (ITR) of 39.8±24.6 bits per minute, which is 86% improvement compared to the baseline method.Clinical Relevance- The proposed Motor Imagery (MI)-BCI system may be of clinical relevance as an active rehabilitation tool for stroke-affected patients, to enhance neural plasticity and recovery of centre-out activities of affected hand and forms a strong platform for MI-BCI coupled with exoskeletons or prosthesis rehabilitation.