Muscle Artifact Removal Toward Mobile SSVEP-Based BCI: A Comparative Study

Abstract

Steady-state visual evoked potential (SSVEP) serves as one of the extensively utilized paradigms for brain–computer interface (BCI). SSVEP-based BCI has advantages of good classification accuracy, high information transfer rate (ITR), and little user training. However, there exist few studies investigating SSVEP-based BCI in the mobile situation, where electroencephalography (EEG) data are inevitably contaminated by muscle activities, and meanwhile, a limited number of channels are preferred. In this study, two major muscle artifact removal schemes are investigated in the dynamic scenario. One scheme adopts state-of-the-art blind source separation (BSS) methods without electromyogram (EMG) reference, while the other utilizes auxiliary EMG channels as a reference in the denoising process. To evaluate the performance of two schemes, we have tested them on real EEG data collected from ten subjects. The results demonstrate that, in the few-channel condition of SSVEP data, there are classification accuracy improvements of 9.93% for the canonical correlation analysis (CCA) indicator and 15.72% for the task-related component analysis (TRCA) indicator after processed by recursive least-squares (RLS) filtering with auxiliary EMG, and in the single-channel condition, there are classification accuracy improvements of 22.00% for the CCA indicator and 24.42% for the TRCA indicator after RLS filtering, averaged across all the subjects and data lengths. However, the BSS-based methods fail to improve the classification accuracies of SSVEP. Thus, it is of necessity to mount additional EMG channels for effective muscle activity suppression. This study provides an insightful suggestion for future real-life BCI applications.