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http://dx.doi.org/10.5391/JKIIS.2013.23.2.172

EEG Analysis Following Change in Hand Grip Force Level for BCI Based Robot Arm Force Control  

Kim, Dong-Eun (School of Electrical and Electronics Engineering, Chung-Ang University)
Lee, Tae-Ju (School of Electrical and Electronics Engineering, Chung-Ang University)
Park, Seung-Min (School of Electrical and Electronics Engineering, Chung-Ang University)
Ko, Kwang-Eun (School of Electrical and Electronics Engineering, Chung-Ang University)
Sim, Kwee-Bo (School of Electrical and Electronics Engineering, Chung-Ang University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.23, no.2, 2013 , pp. 172-177 More about this Journal
Abstract
With Brain Computer Interface (BCI) system, a person with disabled limb could use this direct brain signal like electroencephalography (EEG) to control a device such as the artifact arm. The precise force control for the artifact arm is necessary for this artificial limb system. To understand the relationship between control EEG signal and the gripping force of hands, We proposed a study by measuring EEG changes of three grades (25%, 50%, 75%) of hand grip MVC (Maximal Voluntary Contract). The acquired EEG signal was filtered to obtain power of three wave bands (alpha, beta, gamma) by using fast fourier transformation (FFT) and computed power spectrum. Then the power spectrum of three bands (alpha, beta and gamma) of three classes (MVC 25%, 50%, 75%) was classified by using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The result showed that the power spectrum of EEG is increased at MVC 75% more than MVC 25%, and the correct classification rate was 52.03% for left hand and 77.7% for right hand.
Keywords
Brain Computer Interface; EEG; Robot Arm Force Control; PCA; LDA;
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