Hurst exponents and linear regression with an application to low-power beta characterization in meditation EEG.
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.
Chang
Kang-Ming
KM
Department of Electrical and Control Engineering, National Chiao Tung University, Hsinchu, Taiwan, Republic of China.
Lo
Pei-Chen
PC
D000465:Algorithms; D001611:Beta Rhythm / Q000379:methods*; D001921:Brain / Q000502:physiology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016014:Linear Models; D019122:Meditation; D010363:Pattern Recognition, Automated / Q000379:methods*