標題: A Novel Classification Method for Motor Imagery Based on Brain-Computer Interface
作者: Chen, Chih-Yu
Wu, Chun-Wei
Lin, Chin-Teng
Chen, Shi-An
電機學院
腦科學研究中心
College of Electrical and Computer Engineering
Brain Research Center
關鍵字: Brain-Computer Interface (BCI);Motor imagery (MI);electroencephalography (EEG);common spatial pattern (CSP);linear discriminant analysis (LDA)
公開日期: 2014
摘要: Brain computer interface (BCI) is known as a good way to communicate between brain and computer or other device. There are many kinds of physiological signal can operate BCI systems. Motor imagery (MI) has been demonstrated to be a good way to operate a BCI system. In some recent studies about MI based BCI systems, low accuracy rate and time consuming are common problems. In this thesis, a novel motor imagery algorithm is proposed to improve the accuracy rate and computational efficiency at the same time. The architecture of many BCI system is quite complex and they involve time consuming processing. The electroencephalography (EEG) signal is the most commonly used inputs for BCI applications but EEG is often contaminated with noise. To overcome such drawbacks, in this paper we use the common spatial pattern (CSP) for feature extraction from EEG and the linear discriminant analysis (LDA) for motor imagery classification. In this study, CSP and LDA have been used to reduce the artifact and classify MI-based EEG signal. We have used two-level cross validation scheme to determine the subject specific best time window and number of CSP features. We have compared the performance of our system with BCI competition results. This novel algorithm with high accuracy rate and efficiency can be applied to real time BCI system in real-life applications.
URI: http://hdl.handle.net/11536/135068
ISBN: 978-1-4799-1484-5
ISSN: 2161-4393
期刊: PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
起始頁: 4099
結束頁: 4102
Appears in Collections:Conferences Paper