Abstract
One of the procedures often used in an SSVEP-BCI (Steady State Evoked Potential Brain Computer Interface) processing pipeline is multichannel spatial filtering. This procedure not only improves SSVEP-BCI classification accuracy but also provides higher flexibility in choosing the localization of EEG electrodes on the user scalp. The problem is, however, how to choose the spatial filter that provides the highest classification accuracy for the given BCI settings. Although there are some papers comparing filtering procedures, the comparison is usually done in terms of one, strictly defined BCI setup [1, 2]. Such comparisons do not inform, however, whether some filtering procedures are superior to the others regardless of the experimental conditions. The research reported in this paper partially fills this gap. During the research four spatial filtering procedures (MEC, MCC, CCA, and FBCCA) were compared under 15 slightly different SSVEP-BCI setups. The main finding was that none of the procedures showed clear predominance in all 15 setups. By applying not-the-best procedure the classification accuracy dropped significantly, even of more than 30%.
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Rejer, I. (2019). Multichannel Spatial Filters for Enhancing SSVEP Detection. In: Pejaś, J., El Fray, I., Hyla, T., Kacprzyk, J. (eds) Advances in Soft and Hard Computing. ACS 2018. Advances in Intelligent Systems and Computing, vol 889. Springer, Cham. https://doi.org/10.1007/978-3-030-03314-9_41
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DOI: https://doi.org/10.1007/978-3-030-03314-9_41
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