Using Shannon Entropy as EEG Signal Feature for Fast Person Identification
نویسندگان
چکیده
Identification accuracy and speed are important factors in automatic person identification systems. In this paper, we propose a feature extraction method to extract brain wave features from different brain rhythms of electroencephalography (EEG) signal for the purpose of fast, yet accurate person identification. The proposed feature extraction method is based on the fact that EEG signal is complex, non-stationary, and non-linear. With this fact, non-linear analysis like entropy would be more appropriate. Shannon entropy (SE) based EEG features from alpha, beta, and gamma wave bands are extracted and evaluated for person identification. Experimental results show that SE features provide high person identification rates yet with a low feature dimension, thus better performance.
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