TY - GEN
T1 - A novel frequency band selection method for Common Spatial Pattern in Motor Imagery based Brain Computer Interface
AU - Sun, Gufei
AU - Hu, Jinglu
AU - Wu, Gengfeng
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - Brain-Computer Interface (BCI) is a system provides an alternative communication and control channel between the human brain and computer. In Motor Imagery-based (MI) BCI system, Common Spatial Pattern (CSP) is frequently used for extracting discriminative patterns from the electroencephalogram (EEG). There are many studies have proven that the performance of CSP has a very important relation with the choice of operational frequency band. As the fact that the CSP features at different frequency bands contain discriminative and complementary information for classification, this paper proposes a new frequency band selection method to find the best frequency band set on which subject-specifics CSP are complementary for MI classification. Compared to the performance offered by the existing method based on frequency band partition, the proposed algorithm can yield error rate reductions of 49.70% for the same BCI competition dataset.
AB - Brain-Computer Interface (BCI) is a system provides an alternative communication and control channel between the human brain and computer. In Motor Imagery-based (MI) BCI system, Common Spatial Pattern (CSP) is frequently used for extracting discriminative patterns from the electroencephalogram (EEG). There are many studies have proven that the performance of CSP has a very important relation with the choice of operational frequency band. As the fact that the CSP features at different frequency bands contain discriminative and complementary information for classification, this paper proposes a new frequency band selection method to find the best frequency band set on which subject-specifics CSP are complementary for MI classification. Compared to the performance offered by the existing method based on frequency band partition, the proposed algorithm can yield error rate reductions of 49.70% for the same BCI competition dataset.
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U2 - 10.1109/IJCNN.2010.5596474
DO - 10.1109/IJCNN.2010.5596474
M3 - Conference contribution
AN - SCOPUS:79959485237
SN - 9781424469178
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
Y2 - 18 July 2010 through 23 July 2010
ER -