Authors: Sumanta Bhattacharyya; Manoj Kumar Mukul
Addresses: Department of ECE, BIT Mesra, Ranchi, Jharkhand – 835215, India ' Department of ECE, BIT Mesra, Ranchi, Jharkhand – 835215, India
Abstract: 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.
Keywords: brain-computer interface; BCI; electroencephalogram; EEG; movement imagery; MI; reactive frequency band; RFB; short-time Fourier transforms; STFT; temporal relative spectral power; TRSP; wavelet.
International Journal of Intelligent Systems Technologies and Applications, 2018 Vol.17 No.1/2, pp.136 - 152
Received: 26 Feb 2017
Accepted: 25 May 2017
Published online: 03 May 2018 *