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http://dx.doi.org/10.5302/J.ICROS.2007.13.9.838

Study of Analysis of Brain-Computer Interface System Performance using Independent Component Algorithm  

Song, Jung-Wha (한림대학교 정보통신공학부)
Lee, Hyun-Joo (한림대학교 의과대학)
Cho, Bung-Oak (한림대학교 정보통신공학부)
Park, Soo-Young (한림대학교 정보통신공학부)
Shin, Hyung-Cheul (한림대학교 의과대학)
Lee, Un-Joo (한림대학교 정보통신공학부)
Song, Seong-Ho (한림대학교 정보통신공학부)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.13, no.9, 2007 , pp. 838-842 More about this Journal
Abstract
A brain-computer interface(BCI) system is a communication channel which transforms a subject's thought process into command signals to control various devices. These systems use electroencephalographic signals or the neuronal activity of many single neurons. The presented study deals with an efficient analysis method of neuronal signals from a BCI System using an independent component analysis(ICA) algorithm. The BCI system was implemented to generate event signals coding movement information of the subject. To apply the ICA algorithm, we obtained the perievent histograms of neuronal signals recorded from prefrontal cortex(PFC) region during target-to-goal(TG) task trials in the BCI system. The neuronal signals were then smoothed over 5ms intervals by low-pass filtering. The matrix of smoothed signals was then rearranged such that each signal was represented as a column and each bin as a row. Each column was also normalized to have a unit variance. As a result, we verified that different patterns of the neuronal signals are dependent on the target position and predefined event signals.
Keywords
HCI; brain-computer interface; independent component analysis; blind source separation;
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