Electroencephalography (EEG)-based Brain–computer interface (BCI) technology allows a user to control an external device without muscle intervention through recorded neural activity. Ongoing research on BCI systems includes applications in the medical field to assist subjects with impaired motor functionality (e.g., for the control of prosthetic devices). In this context, the accuracy and efficiency of a BCI system are of paramount importance. Comparing four different dimension reduction techniques in combination with linear and nonlinear classifiers, we show that integrating these methods in a BCI system results in a reduced model complexity without affecting overall accuracy.
CITATION STYLE
Cozza, F., Galdi, P., Serra, A., Pasqua, G., Pavone, L., & Tagliaferri, R. (2020). Dimension Reduction Techniques in a Brain–Computer Interface Application. In Smart Innovation, Systems and Technologies (Vol. 151, pp. 107–118). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-13-8950-4_11
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