Abstract
An approach based on machine learning methods to the classification of EEG recorded during 3 type of motor imagery is presented. Moreover, methods for determining the most informative frequency ranges and time characteristics of target movements have been developed based on frequency spectrum calculation and machine learning methods. It is shown that the selection of the optimal frequency ranges separately for each subject and each type of movement, as well as the selection of an informative time segment, significantly increase the classification accuracy. The comparison between 3 different features is given, namely, power spectral densities of several frequency bands, Hjorth parameters and inter-channel correlations. The last two were shown to be advantageous when both search for informative frequency range and search for informative time segment are performed.
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Funding
The research was supported by the RSF project №20-19-00627 “Development of stimulus-unrelated Brain-computer interface for disabled people rehabilitation”.
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Saevskiy, A.I., Shepelev, I.E., Lazurenko, D.M., Shaposhnikov, D.G. (2022). Search for Informative Frequency Range and EEG Time Boundaries for Solving the Problem of Motor Imagery Patterns Classification. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y., Klimov, V.V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research V. NEUROINFORMATICS 2021. Studies in Computational Intelligence, vol 1008. Springer, Cham. https://doi.org/10.1007/978-3-030-91581-0_8
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DOI: https://doi.org/10.1007/978-3-030-91581-0_8
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