Research Article
A Novel Real-Time EEG Based Eye State Recognition System
@INPROCEEDINGS{10.1007/978-3-030-06161-6_17, author={Zijia Zhou and Pan Li and Jianqi Liu and Weikuo Dong}, title={A Novel Real-Time EEG Based Eye State Recognition System}, proceedings={Communications and Networking. 13th EAI International Conference, ChinaCom 2018, Chengdu, China, October 23-25, 2018, Proceedings}, proceedings_a={CHINACOM}, year={2019}, month={1}, keywords={Electroencephalogram (EEG) Eye state identification Feature extraction Wavelet transform Information accumulation algorithm Random forest}, doi={10.1007/978-3-030-06161-6_17} }- Zijia Zhou
Pan Li
Jianqi Liu
Weikuo Dong
Year: 2019
A Novel Real-Time EEG Based Eye State Recognition System
CHINACOM
Springer
DOI: 10.1007/978-3-030-06161-6_17
Abstract
With the development of brain-computer interface (BCI) technology, fast and accurate analysis of Electroencephalography (EEG) signals becomes possible and has attracted a lot of attention. One of the emerging applications is eye state recognition based on EEG signals. A few schemes like the K* algorithm have been proposed which can achieve high accuracy. Unfortunately, they are generally complex and hence too slow to be used in a real-time BCI framework such as an instance-based learner. In this paper, we develop a novel effective and efficient EEG based eye state recognition system. The proposed system consists of four parts: EEG signal preprocessing, feature extraction, feature selection and classification. First, we use the ‘sym8’ wavelet to decompose the original EEG signal and select the 5th floor decomposition, which is subsequently de-noised by the heuristic SURE threshold method. Then, we propose a novel feature extraction method by utilizing the information accumulation algorithm based on wavelet transform. By using the CfsSubsetEval evaluator based on the BestFirst search method for feature selection, we identify the optimal features, i.e., optimal scalp electrode positions with high correlations to eye states. Finally, we adopt Random Forest as the classifier. Experiment results show that the accuracy of the overall EEG eye state recognition system can reach 99.8% and the minimum number of training samples can be kept small.
