EEG-based BCI: A novel improvement for EEG signals classification based on real-time preprocessing

This work aims to improve EEG signal binary and multiclass classification for real-time BCI applications. Therefore, our paper discusses the results of a new real-time approach that was integrated into a complete prediction system, where we proposed a new trick to eliminate the effect of EEG's non-stationarity nature. This improvement can increase the accuracy from 50% using raw EEG to the order of 90% after preprocessing step in the binary case and from 28% to 78% in the multiclass case. Then, we chose to filter all signals by the proposed bandpass filter automatically optimized using the sine cosine algorithm (SCA) to find the optimal bandwidth that contains the entire EEG characteristics in beta waves. Moreover, we used a common spatial pattern (CSP) filter to eliminate the correlation between all extracted features. Then, the light gradient boosting machine (LGBM) classifier is also combined with SCA algorithm to build better prediction models. As a result, the outcome system was applied on UCI and PhysioNet datasets to get excellent accuracy values of higher than 99% and 95%, respectively, using the data acquired only from three channels. On the other hand, the related works used all the data acquired from 14 channels to find an accuracy value between 70% and 98.5%, which shows the robustness of our method to improve EEG signal prediction quality.

Automated summary

This work aims to improve the binary and multiclass classification of EEG signals for real-time BCI applications. With a new real-time approach, the accuracy of binary and multiclass classification is significantly increased after preprocessing. The proposed bandpass filter and common spatial pattern filter are used to further enhance the prediction models.