TY - GEN
T1 - Sub-band common spatial pattern (SBCSP) for brain-computer interface
AU - Novi, Quadrianto
AU - Guan, Cuntai
AU - Dat, Tran Huy
AU - Xue, Ping
PY - 2007
Y1 - 2007
N2 - Brain-computer interface (BCI) is a system to translate humans thoughts into commands. For Electroencephalography (EEG) based BCI, motor imagery is considered as one of the most effective ways. Different imagery activities can be classified based on the changes in μ and/or β rhythms and their spatial distributions. However, the change in these rhythmic patterns varies from one subject to another. This causes an unavoidable time-consuming fine-tuning process in building a BCI for every subject. To address this issue, we propose a new method called Sub-band Common Spatial Pattern (SBCSP) to solve the problem. First, we decompose the EEG signals into sub-bands using a filter bank. Subsequently, we apply a discriminative analysis to extract SBCSP features. The SBCSP features are then fed into Linear Discriminant Analyzers (LDA) to obtain scores which reflect the classification capability of each frequency band. Finally, the scores are fused to make decision. We evaluate two fusion methods: Recursive Band Elimination (RBE) and Meta-Classifier (MC). We assess our approaches on a standard database from BCI Competition III. We also compare our method with two other approaches that address the same issue. The results show that our method outperforms the other two approaches and achieves similar result as compared to the best one in the literature which was obtained by a time-consuming fine-tuning process.
AB - Brain-computer interface (BCI) is a system to translate humans thoughts into commands. For Electroencephalography (EEG) based BCI, motor imagery is considered as one of the most effective ways. Different imagery activities can be classified based on the changes in μ and/or β rhythms and their spatial distributions. However, the change in these rhythmic patterns varies from one subject to another. This causes an unavoidable time-consuming fine-tuning process in building a BCI for every subject. To address this issue, we propose a new method called Sub-band Common Spatial Pattern (SBCSP) to solve the problem. First, we decompose the EEG signals into sub-bands using a filter bank. Subsequently, we apply a discriminative analysis to extract SBCSP features. The SBCSP features are then fed into Linear Discriminant Analyzers (LDA) to obtain scores which reflect the classification capability of each frequency band. Finally, the scores are fused to make decision. We evaluate two fusion methods: Recursive Band Elimination (RBE) and Meta-Classifier (MC). We assess our approaches on a standard database from BCI Competition III. We also compare our method with two other approaches that address the same issue. The results show that our method outperforms the other two approaches and achieves similar result as compared to the best one in the literature which was obtained by a time-consuming fine-tuning process.
UR - http://www.scopus.com/inward/record.url?scp=34548798641&partnerID=8YFLogxK
U2 - 10.1109/CNE.2007.369647
DO - 10.1109/CNE.2007.369647
M3 - Conference contribution
SN - 1424407923
SN - 9781424407927
T3 - Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering
SP - 204
EP - 207
BT - Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering
T2 - 3rd International IEEE EMBS Conference on Neural Engineering
Y2 - 2 May 2007 through 5 May 2007
ER -