Abstract
A spectral power-based real-time BCI neuro-feedback (decision machine) system has been proposed in this paper. It is used to reflect the intended task of the movement imagination of hand movements. As a consequence of the cost-effective and greater temporal resolution, the proposed decision machine considered the electroencephalogram (EEG) signal. EEG data modeling is therefore an important step in seeking a rational solution for a specific application. The most potential temporal relative spectral power (TRSP) based feature extraction algorithm, and probabilistic Bayesian classifier has been used to model the EEG data for the reproduction of the thoughts. In this paper, the author has considered two channels of information for command generation. This paper also focuses on the maximization of information retrieval by analyzing incoming two channels' brain signals. This initiates to devise an appropriate reactive frequency band for each subject before applying the feature extraction algorithm. In this direction, this paper uses relative power spectral intensity (RPSI) for reactive frequency band estimation. The spectral power-based feature extraction method is responsible for the improvement of the accuracy, reducing the computational complexity, lowers the computational time, and improves the information transfer rate. The performance of the proposed BCI system shows better results compared to the different well established conventional EEG based BCI systems. The proposed real-time neuro-feedback system is a generalized BCI system that will be readily applicable for controlling any external devices. The proposed system shows 90.37% accuracy over the BCI Competition II dataset and 81% average accuracy with 64 ms computational time in the real-time application.
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The authors thank the Dept of ECE, NIT Mizoram for the support to carry out the work.
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Bhattacharyya, S., Das, S., Das, A. et al. Neuro-feedback system for real-time BCI decision prediction. Microsyst Technol 27, 3725–3734 (2021). https://doi.org/10.1007/s00542-020-05146-4
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DOI: https://doi.org/10.1007/s00542-020-05146-4