脑电近似熵分析的思维分类识别

Journal of Applied Sciences ›› 2006, Vol. 24 ›› Issue (4): 359-362.

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Mental Task Classification Based on Approximate Entropy of EEG

JIANG Zhao-hui¹, GAO Cui-yun², FENG Huan-qing¹

1. Department of ElectronicScience & Technology, University of Science and Technology of China, Hefei 230026, China;
2. Department of Computer & Information Engineering, Anhui Institute of Architecture & Industry, Hefei 230022, China

Received:2005-01-15 Revised:2005-04-18 Online:2006-07-31 Published:2006-07-31

Abstract

Abstract: Mental activity is closely related to the complexity of electroencephalogram (EEG) in different areas of brain. In order to classify five different kinds of mental tasks, approximate entropy (ApEn), an index of complexity, is calculated from six channels EEG and serve as input feature vector to a neural network.Testing results show that the average classification accuracy is 73% obtained from the average of 5 trials, indicating feasibility of classifying different mental tasks based on complexity of multi-channel EEG.The introduced technique is useful in the research of human machine interface.

Key words: electroencephalogram (EEG), mental task, approximate entropy (ApEn), neural network

CLC Number:

R318.04

JIANG Zhao-hui, GAO Cui-yun, FENG Huan-qing. Mental Task Classification Based on Approximate Entropy of EEG[J]. Journal of Applied Sciences, 2006, 24(4): 359-362.

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Mental Task Classification Based on Approximate Entropy of EEG

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