Paper Publications
Release time: 2026-10-01Hits:
- Journal:中国生物医学工程学报
- Key Words:filter design，motor imagery; brain-computer interface ( BCI) ; common spatial patterns ( CSP) ; iterative optimization
- Abstract:The motor imagery-based brain-computer interface (BCI) system is an important research theme in BCIs. A popular feature extraction method for motor imagery data analysis is the common spatial patterns (CSP ) algorithm. The performance of the CSP feature extraction is contingent on appropriate band-pass filtering，which usually highly depends on the prior neurophysiologic knowledge. In this paper we present an algorithm termed iterative common spatial-temporal patterns ( ICSTP) for learning spatio-temporal features from motor imagery EEG data. The algorithm optimizes temporal filters by solving a generalized eigenvalue problem in the same way as CSP does in learning spatial filters. A proof for the convergence of the algorithm is provided. Experimental results on real EEG data demonstrate that the algorithm can converge rapidly within a few iterations. The average accuracy is higher than that of standard CSP using manually chosen temporal filters.
- Indexed by:Journal paper
- Volume:30
- Issue:1
- Translation or Not:no
- Date of Publication:2011-01-02
- DOI number:10.3969/j.issn.0258-8021.2011.01.03
- First Author:Duan, Fang
- Correspondence Author:Gao, Xiaorong
MOBILE Version