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Reactive frequency band-based real-time motor imagery classification

The requirement of an effective online processing algorithm becomes very vital to fulfilling the demand of the low-cost brain–computer interface (BCI) system. The authors proposed a very first and robust unsupervised machine learning algorithm, for the real-time classification of movement imagination. The reactive frequency band (RFB) of the individual subject has been identified through the dominant frequency detection algorithm over the training dataset. Based on the identified RFB, the feature extraction process has been applied to the testing dataset. The estimated 'feature' further classified as per probabilistic Bayesian classifier. The effectiveness of the proposed RFB detection method of electroencephalogram (EEG) signal is validated by self-generated artificial sine wave signal, single subject and nine subject movement imagery (MI) BCI competition dataset. The proposed method of EEG signal processing outperformed the conventional wavelet-based BCI competition II results and the wavelet-based algorithm applied over the BCI competition IV dataset.