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| Hand Motion Classification Based on Eye-Moving Assisted EEG |
| MENG Ming, LUO Zhi-Zeng |
| Institute of Intelligent Control and Robotics,Hangzhou Dianzi University,Hangzhou 310018 |
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Abstract A classification method using wavelet packet transform (WPT) and support vector machine (SVM) is presented to classify the motor imagery electroencephalogram(EEG)of hand motion. Firstly, the relevant eye-moving assisted EEG at C3, C4, P3 and P4 during hand-motion imagery are recorded. Then, four feature rhythm waves are extracted using WPT, and the ratio of energy of each rhythm wave to the sum energy of all four rhythm waves is calculated respectively as the feature. Finally, the 16 dimension feature vector is input into SVM classifier to recognize the hand-motions. The average correct rate of four patterns of hand motions, namely wrist extension, wrist flexion, hand opening and hand grasping, is 82.3% in classification experiments and it shows that eye-moving assist improves the separability of motor imagery EEG.
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Received: 14 November 2011
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2012, Vol. 25 
