We propose an algorithm for the operation of a brain–computer interface based on recording hemodynamic activity using near-infrared spectrometry (NIRS) adapted for use in the rehabilitation of motor disorders. The algorithm includes a filtration method that takes account of the frequency of presentation of instructions with the aim of minimizing the time delay in the target frequency range, sequential classification of the states of rest and the mental tasks being performed, as well as additional training of the interface classifier using previously obtained data. The influences of the proposed methods for filtration, additional training, and selection of a smaller number of channels on the accuracy of recognition of motor imagery is evaluated using data from three series of experiments previously conducted in the laboratory with healthy volunteers. Removal of low-frequency noise components from the NIRS signal using the proposed filtration method is shown to produce significant increases in classification accuracy, as did use of additional training from previous sessions in the same person; the number of recording channels could be decreased without loss of recognition accuracy.
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Translated from Zhurnal Vysshei Nervnoi Deyatel’nosti imeni I. P. Pavlova, Vol. 72, No. 5, pp. 728–738, September–October, 2022.
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Isaev, M.R., Bobrov, P.D. Effects of Selection of the Learning Set Formation Strategy and Filtration Method on the Effectiveness of a BCI Based on Near Infrared Spectrometry. Neurosci Behav Physi 53, 373–380 (2023). https://doi.org/10.1007/s11055-023-01436-2
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DOI: https://doi.org/10.1007/s11055-023-01436-2