Feature Analysis of EEG Based Brain-Computer Interfaces to Detect Motor Imagery

Abstract

Brain-Computer Interfaces (BCI) is one of the alluring breakthroughs for mankind as it provides a new way of communication for the patients of neuro-muscular disorders. Electroencephalography (EEG) signals are the most studied type of signals to detect brain activities because of its non-invasive and portable nature. The major problem in the identification of neural activities from EEG signals and the presence of non-task related artifacts in the signal data. These artifacts affect the classification of feature set. With these effective techniques, BCI classifier can efficiently classify EEG signals. The proposed research deals with different motor imagery datasets for the detection of movements. An EEG based BCI system is proposed that implement a linear regression based artifact removal method for EOG processing, feature construction and recursive feature elimination with cross-validation. It achieved promising results with relatively fewer data used for training than the original competition’s data, that shows the significance as compared to top leaderboard entries. The results obtained show that our approach tackles noise and artifacts in EEG signals which provides reliable features for BCI classification.