EEG-Based Familiar and Unfamiliar Face Classification Using Differential Entropy Feature

被引:0
|
作者
Liu, Guoyang [1 ]
Di Zhang [1 ]
Tian, Lan [1 ]
Zhou, Weidong [1 ]
机构
[1] Shandong Univ, Sch Microelect, Jinan, Shandong, Peoples R China
来源
PROCEEDINGS OF THE 2021 IEEE INTERNATIONAL CONFERENCE ON HUMAN-MACHINE SYSTEMS (ICHMS) | 2021年
关键词
familiar and unfamiliar face classification; electroencephalography (EEG); differential entropy; brain computer interface (BCI); feature extraction; RECOGNITION;
D O I
10.1109/ICHMS53169.2021.9582641
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
This study presents a novel approach for familiar and unfamiliar face classification based on electroencephalography (EEG). Firstly, the raw EEG epoch is temporally split into three overlapped segments, and each segment is decomposed into multiple sub-bands by band-pass filters. Then, differential entropy is employed to extract discriminative EEG features. Finally, the obtained features are concatenated and classified with the support vector machine (SVM). The results yielded on our database indicate that the proposed method can achieve a mean accuracy of 76.2% over five participants. This work primarily demonstrates that differential entropy is an effective feature for EEG-based familiar and unfamiliar face classification, and has the potential to be applied to other EEG-based visual task analyses.
引用
收藏
页码:190 / 192
页数:3
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