Fuzzy Divergence Based Analysis for Eeg Drowsiness Detection Brain Computer Interfaces

EEG signals can be processed and classified into commands for brain-computer interface (BCI). Stable deciphering of EEG is one of the leading challenges in BCI design owing to low signal to noise ratio and non-stationarities. Presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. Stationary Subspace methods discover subspaces in which data distribution remains steady over time. In this paper, we develop novel spatial filtering based feature extraction methods for dealing with nonstationarity in EEG signals from a drowsiness detection problem (a machine learning regression problem). The proposed method: DivOVR-FuzzyCSP-WS based features clearly outperformed fuzzy CSP based baseline features in terms of both RMSE and CC performance metrics. It is hoped that the proposed feature extraction method based on DivOVR-FuzzyCSP-WS will bring in a lot of interest in researchers working in developing algorithms for signal processing, in general, for BCI regression problems.