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EEG feature selection method based on decision tree

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Duan, Lijuana; c | Ge, Huia; c | Ma, Weia; c | Miao, Junb; *

Affiliations: [a] Key Laboratory of Trusted Computing, Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, College of Computer Science and Technology, Beijing University of Technology, Beijing, 100124, China | [b] Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China | [c] National Engineering Laboratory for Critical Technologies of Information Security Classified Protection, Beijing 100124, China

Correspondence: [*] Address for correspondence: Jun Miao, Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China. Tel.: 62600556; Fax: 62600523; E-mail: [email protected].

Keywords: Decision tree, feature selection, optimal features, EEG, brain-computer interface

DOI: 10.3233/BME-151397

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1019-S1025, 2015

Published: 2015
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This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

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