Multiclass Fuzzy Time-Delay Common Spatio-Spectral Patterns With Fuzzy Information Theoretic Optimization for EEG-Based Regression Problems in Brain–Computer Interface (BCI)

Abstract

Electroencephalogram (EEG) signals are one of the most widely used noninvasive signals in brain–computer interfaces. Large dimensional EEG recordings suffer from poor signal-to-noise ratio. These signals are very much prone to artifacts and noise, so sufficient preprocessing is done on raw EEG signals before using them for classification or regression. Properly selected spatial filters enhance the signal quality and subsequently improve the rate and accuracy of classifiers, but their applicability to solve regression problems is quite an unexplored objective. This paper extends common spatial patterns (CSP) to EEG state space using fuzzy time delay and thereby proposes a novel approach for spatial filtering. The approach also employs a novel fuzzy information theoretic framework for filter selection. Experimental performance on EEG-based reaction time (RT) prediction from a lane-keeping task data from 12 subjects demonstrated that the proposed spatial filters can significantly increase the EEG signal quality. A comparison based on root-mean-squared error (RMSE), mean absolute percentage error (MAPE), and correlation to true responses is made for all the subjects. In comparison to the baseline fuzzy CSP regression one versus rest, the proposed Fuzzy Time-delay Common Spatio-Spectral filters reduced the RMSE on an average by $\text{9.94}\%$ , increased the correlation to true RT on an average by $\text{7.38}\%$ , and reduced the MAPE by $\text{7.09}\%$ .