An Adaptive EEG filtering approach to maximize the classification accuracy in motor imagery

We propose in this paper a novel approach of adaptive filtering of EEG signals. The filter adapts to the intrinsic characteristics of each person. The goal of the proposed method is to enhance the accuracy of the home devices system controlled by the thoughts related to two motor imagery actions. mu-rhythm and beta-rhythm are the specific returned bands that contain the information. The main idea of the proposed method is to preserve the frequency bands of interest with a different value of the SNR on the stop-band. Our experimental results show the benefits of a suitable tuning of the filter on the accuracy of the classifier on the output of the EEG system. The proposed approach outperforms significantly performances reported in the literature and the effectively enhancement of the classification accuracy can reach up to 40% based only on filtering tuning.