A feature extraction technique based on tunable Q-factor wavelet transform for brain signal classification.

Affiliation

Faculty of Health, Engineering and Sciences, University of Southern Queensland, QLD, 4350, Australia; College of Computer Sciences and Mathematics, University of Thi-Qar, 64001, Iraq. Electronic address: [Email]

Abstract

Electroencephalogram (EEG) signals are important for brain health monitoring applications. Characteristics of EEG signals are complex, being non-stationarity, aperiodic and nonlinear in nature. EEG signals are a combination of sustained oscillation and non-oscillation transients that are challenging to deal with using linear approaches.

Keywords

Classification,Electroencephalogram (EEG) signal,Epilepsy,Tunable Q-factor wavelet transform,