@inproceedings{65628ac8634d405fafc66f860a66a0af,
title = "The offline feature extraction of four-class motor imagery EEG based on ICA and Wavelet-CSP",
abstract = "The signal processing of electroencephalogram (EEG) is the key technology in a brain-computer interface (BCI) system. A widely used method is to purify the raw EEG with an 8-30Hz band-pass filter and extract features by common spatial patterns (CSP). However its results for BCI Competition IV are not very satisfactory. To improve the classification success rate, this paper proposed a novel Wavelet-CSP with ICA-filter method. For the data sets from BCI Competition IV, the features of the four-class motor imagery were trained and tested using the Support Vector Machines (SVM). The experimental results showed that the proposed method had a higher average kappa coefficient of 0.68 than 0.52 of the general method.",
keywords = "Brain-computer-interface (BCI), ICA, SVM, Wavelet-CSP, electroencephalogram (EEG)",
author = "Xiaoping Bai and Xiangzhou Wang and Shuhua Zheng and Mingxin Yu",
note = "Publisher Copyright: {\textcopyright} 2014 TCCT, CAA.; Proceedings of the 33rd Chinese Control Conference, CCC 2014 ; Conference date: 28-07-2014 Through 30-07-2014",
year = "2014",
month = sep,
day = "11",
doi = "10.1109/ChiCC.2014.6896188",
language = "English",
series = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",
publisher = "IEEE Computer Society",
pages = "7189--7194",
editor = "Shengyuan Xu and Qianchuan Zhao",
booktitle = "Proceedings of the 33rd Chinese Control Conference, CCC 2014",
address = "United States",
}