Abstract
Distinct feature extraction methods are simultaneously used to describe single channel Electroencephalography (EEG) based biometrics. This study proposes a new strategy to extract features from EEG signals. Based on the time and frequency information, the statistics features are obtained from the EEGs. For the dichotomize process, the support vector machine classifier is used with 10-cross-fold in this research. The main contribution of this paper is to propose a simple but effective single-channel EEG feature extraction method and consider feature selection to optimize classification efficiency. In the experiments, the EEG data is obtained from a human-computer interaction environment when the subjects are under the non-stationary states with different emotions. The results show that this proposed method achieves better classification performance on a single-channel EEG system than previous work.
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