Abstract
Brain–computer interface systems are communicative mediums between human brain and external device. One of the applications of these systems is P300 speller. This application provides the ability to spell the characters on the screen for disabled people. In this study, we review the character recognition and its relation to P300 detection. Then, we used three neural networks models with flexible activation functions to detect P300 patterns from electroencephalographic signals more accurately. The obtained results have shown the accuracy of the character recognition based on the precision and recall measures.
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Mousavi, S.A., Arshad, M.R.H.M., Mohamed, H.H., Sumari, P., Fard, S.P. (2014). P300 Detection in Electroencephalographic Signals for Brain–Computer Interface Systems: A Neural Networks Approach. In: Wong, W.E., Zhu, T. (eds) Computer Engineering and Networking. Lecture Notes in Electrical Engineering, vol 277. Springer, Cham. https://doi.org/10.1007/978-3-319-01766-2_41
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DOI: https://doi.org/10.1007/978-3-319-01766-2_41
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