Abstract
Brain-Computer Interface is aimed as a direct communication pathway between human or animal brain and an external device. A reliable, accurate and fast identification of a being’s intention based on EEG signal scanning is crucial part of the system. To improve the classification accuracy we propose to use Independent Component Analysis for \(\mu \)-rhythm identification in data corresponding to motor imagery task performance during Brain-Computer Interface training and operation. We show that independent components corresponding to the \(\mu \)-rhythm allow for higher classification accuracy comparing to raw EEG recordings usage.
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Acknowledgments
This paper has been partly elaborated in the framework of the IT4Innovations Centre of Excellence project, reg. no. CZ.1.05/1.1.00/02.0070, supported by Operational Programme ‘Research and Development for Innovations’ funded by Structural Funds of the European Union and state budget of the Czech Republic and partly supported by the projects AV0Z10300504, GACR P202/10/0262, 205/09/1079.
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Bobrov, P., Frolov, A.A., Húsek, D. (2013). Brain Computer Interface Enhancement by Independent Component Analysis. In: Kudělka, M., Pokorný, J., Snášel, V., Abraham, A. (eds) Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011. Advances in Intelligent Systems and Computing, vol 179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31603-6_5
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DOI: https://doi.org/10.1007/978-3-642-31603-6_5
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