EEG-Based Age and Gender Recognition Using Tensor Decomposition and Speech Features

Phuoc Nguyen, Dat Tran, Tan Vo, Xu Huang, Wanli Ma, Dinh Q. Phung

Abstract: Extracting age and gender information from EEG data has not been investigated. This information is useful in building automatic systems that can classify a person into gender or age groups based on EEG characteristics of that person, index EEG data for searching, identify or verify a person, and improve performance of brain-computer interface systems. In this paper, we propose a framework based on PARAFAC and SVM that can automatically classify age and gender using EEG data. We also propose a method using N-PLS and SVM to improve the classification rate. Experimental results for the proposed method are presented.