EEG Recognition of Motor Imagery Based on EWT in Driving Assistance
Wei Shan, Yuwen Wang, Qun He, Ping Xie
Available Online December 2017.
- https://doi.org/10.2991/mcei-17.2017.3How to use a DOI?
- Empirical wavelet transform; Motor imagery; Feature extraction; Pattern recognition; Driving assistance.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017) PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/mcei-17.2017.3 DO - https://doi.org/10.2991/mcei-17.2017.3 ID - Shan2017/12 ER -