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http://dx.doi.org/10.5515/KJKIEES.2018.29.7.508

SVM-Based EEG Signal for Hand Gesture Classification  

Hong, Seok-min (Department of Secured Smart Electric Vehicle, Kookmin University)
Min, Chang-gi (Department of Secured Smart Electric Vehicle, Kookmin University)
Oh, Ha-Ryoung (Department of Secured Smart Electric Vehicle, Kookmin University)
Seong, Yeong-Rak (Department of Secured Smart Electric Vehicle, Kookmin University)
Park, Jun-Seok (Department of Secured Smart Electric Vehicle, Kookmin University)
Publication Information
The Journal of Korean Institute of Electromagnetic Engineering and Science / v.29, no.7, 2018 , pp. 508-514 More about this Journal
Abstract
An electroencephalogram (EEG) evaluates the electrical activity generated by brain cell interactions that occur during brain activity, and an EEG can evaluate the brain activity caused by hand movement. In this study, a 16-channel EEG was used to measure the EEG generated before and after hand movement. The measured data can be classified as a supervised learning model, a support vector machine (SVM). To shorten the learning time of the SVM, a feature extraction and vector dimension reduction by filtering is proposed that minimizes motion-related information loss and compresses EEG information. The classification results showed an average of 72.7% accuracy between the sitting position and the hand movement at the electrodes of the frontal lobe.
Keywords
EEG; SVM; STFT; Hand Gesture; Kernel Function;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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