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Hurst exponents and linear regression with an application to low-power beta characterization in meditation EEG
1Department of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan, Republic of China.
American Journal of Electroneurodiagnostic Technology
|July 2, 2005
Abstract:
A lever-like EEG feature-extraction method based on the Hurst exponent and regression-fitting errors is proposed for identifying beta rhythms. The proposed method is superior to most methods using the time- and frequency-domain feature extraction parameters for identifying beta rhythms.
