Noise Suppression by Minima Controlled Recursive Averaging for SSVEP-Based BCIs With Single Channel

Abstract

Subjects with amyotrophic lateral sclerosis (ALS) consistently experience decreasing quality of life because of this distinctive disease. Thus, a practical brain-computer interface (BCI) application can effectively help subjects with ALS to participate in communication. In practices, the noise would greatly reduce the performance of BCIs. In this study, minima controlled recursive averaging is applied to suppress noise and improve the performance of practical BCI applications. Minima controlled recursive averaging is used to correctively track the noise. To suppress these noises, a log-spectral amplitude estimator is selected as the gain function and used to effectively estimate the power spectrum of the noises. Eight subjects were asked to attend a performance test of the proposed approach and the canonical correlation analysis (CCA) was adopted to compare the proposed approach. The average recognition rates based on single channel are 69.57% and 74.63% for CCA and proposed approach, respectively. The experimental results demonstrated that our approach is able to improve performance in practice.