Abstract
Communication between machine and human is one of important application of brain-computer interface (BCI). There are still obtained many kinds of noise in the recorded human signals, specifically brain signal is electroencelophagram (EEG). It caused by outer and inner of the brain signals such as artifacts in signal, properties of EEG signal nonstationary, variant by time and subjects, which affect to the classification results. The most famous spatial filter in BCI context is common spatial patterns (CSP), maximize one condition while minimize the other condition using covariance. So in this experiment we recorded signal by using auditory stimuli to reduce artifact by gaze attention. Extended CSP methods were applied in this experiment to upgrade the classification accuracy of brain source separate by independent component analysis (ICA). We supposed this combination could purify the signals as 2 steps and increased the accuracy classification.
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Nguyen, T.H., Park, SM., Ko, KE., Sim, KB. (2012). Improvement of Spatial Filtering by Using ICA in Auditory Stimuli BCI Systems of Hand Movement. In: Lee, G., Howard, D., Kang, J.J., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2012. Lecture Notes in Computer Science, vol 7425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32645-5_60
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DOI: https://doi.org/10.1007/978-3-642-32645-5_60
Publisher Name: Springer, Berlin, Heidelberg
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