EEG Recognition of Motor Imagery Based on EWT in Driving Assistance

DOI

10.2991/mcei-17.2017.3How to use a DOI?

Keywords

Empirical wavelet transform; Motor imagery; Feature extraction; Pattern recognition; Driving assistance.

Abstract

Electroencephalogram (EEG) feature extraction is one of the key techniques for Brain Computer Interface (BCI) in driving assistance. In this paper, empirical wavelet transform (EWT) is introduced for EEG feature extraction, and then a new EEG recognition method based on EWT is proposed. In the proposed method, the EEG features of the left arm flexion and extension motor imagery mode were first extracted via EWT and compared with those extracted by the empirical mode decomposition (EMD), and then the extracted features were classified by support vector machine (SVM). The results show that the EWT method significantly outperforms the EMD method, which can effectively decompose the intrinsic modes of EEG, and it presents several merits of fewer modes, no false mode and low computation cost. Therefore, the EWT method can effectively extract the local transient features of EEG and realize the effective classification through SVM, thus providing the chance of driving assistance through the arm flexion and extension imagination.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wei Shan
AU  - Yuwen Wang
AU  - Qun He
AU  - Ping Xie
PY  - 2017/12
DA  - 2017/12
TI  - EEG Recognition of Motor Imagery Based on EWT in Driving Assistance
BT  - Proceedings of the 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)
PB  - Atlantis Press
SP  - 11
EP  - 15
SN  - 2352-538X
UR  - https://doi.org/10.2991/mcei-17.2017.3
DO  - 10.2991/mcei-17.2017.3
ID  - Shan2017/12
ER  -