A frequency-temporal-spatial method for motor-related electroencephalography pattern recognition by comprehensive feature optimization

Authors:
Bian Wu;Fan Yang;Jicai Zhang;Yiwen Wang;Xiaoxiang Zheng;Weidong Chen
Affiliations:
Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China and Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China and Key Laboratory of Biomedi ...;Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China and College of Computer Science, Zhejiang University, Hangzhou 310027, China;Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China and College of Computer Science, Zhejiang University, Hangzhou 310027, China;Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China;Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China and Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China and Key Laboratory of Biomedi ...;Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China and College of Computer Science, Zhejiang University, Hangzhou 310027, China
Venue:
Computers in Biology and Medicine
Year:
2012

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Abstract

Either imagined or actual movements lead to a combination of electroencephalography signals with distinctive frequency, temporal and spatial characteristics, which correspond to various motor-related neural activities. This frequency-temporal-spatial pattern is the key of motor intention decoding which is the basis of brain-computer interfaces by motor imagery. We present a new method for motor-related electroencephalography recognition which comprehensively optimizes the frequency-time-space features in a user-specific way. The recognition work focuses on three points: proper time and frequency domain segmentation, spatial optimization based on common spatial pattern filters and feature importance evaluation. We show that by combining the advantages of these optimizational methods, the proposed algorithm effectively improves motor task classification, and the recognized signal chanracteristics can be used to visualize the motor related electroencephalography patterns under different conditions.