Affiliation:
1. School of Electronic and Information Engineering, Beihang University, Beijing, China
Abstract
Electroencephalogram data are easily affected by artifacts, and a drift may occur during the signal acquisition process. At present, most research focuses on the automatic detection and elimination of artifacts in electrooculograms, electromyograms and electrocardiograms. However, electroencephalogram drift data, which affect the real-time performance, are mainly manually calibrated and abandoned. An emotion classification method based on 1/f fluctuation theory is proposed to classify electroencephalogram data without removing artifacts and drift data. The results show that the proposed method can still achieve a great classification accuracy of 75% in cases in which artifacts and drift data exist when using the support vector machine classifier. In addition, the real-time performance of the proposed method is guaranteed.
Funder
the National Science Foundation for Young Scientists of China
fundamental research funds for the central universities
Specialized Research Fund for the Doctoral Program of Higher Education
Subject
Applied Mathematics,Control and Optimization,Instrumentation
Cited by
4 articles.
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