DYNAMICALLY WEIGHTED CLASSIFICATION WITH CLUSTERING TO TACKLE NON-STATIONARITY IN BRAIN COMPUTER INTERFACING

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DYNAMICALLY WEIGHTED CLASSIFICATION WITH CLUSTERING TO TACKLE NON-STATIONARITY IN BRAIN COMPUTER INTERFACING
Title:
DYNAMICALLY WEIGHTED CLASSIFICATION WITH CLUSTERING TO TACKLE NON-STATIONARITY IN BRAIN COMPUTER INTERFACING
Journal Title:
The 2012 International Joint Conference on Neural Networks (IJCNN)
Keywords:
Publication Date:
10 June 2012
Citation:
S. R. Liyanage, Cuntai Guan, Haihong Zhang, Kai Keng Ang, Jian-Xin Xu and Tong Heng Lee, "Dynamically Weighted Classification with Clustering to tackle non-stationarity in Brain computer Interfacing," The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, QLD, 2012, pp. 1-6. doi: 10.1109/IJCNN.2012.6252652
Abstract:
The performance of Brain-Computer Interface (BCI) applications are sometimes hindered by non-stationarity in the EEG data. This paper proposes a novel Dynamically Weighted Classification with Clustering (DWCC) to handle the non-stationarity in the EEG data. The proposed method employs cosine distance based and mutual Information-based criteria in a k-means clustering framework. The proposed method addresses the non-stationarity in EEG since non-stationary sources form clusters in the feature space that are distinctly apart from one another. Multiple classifiers are trained independently based on these clustered features. A dynamically-weighted classifier ensemble approach is proposed to perform the classification. The dynamic weights are based on the distances from a testing sample to cluster centres. Experimental results on publicly available BCI Competition IV Dataset 2a yielded a mean accuracy of 81.5% which is statistically significant (t-test p<0.05) compared to the baseline result of 75.9% using a single classifier.
License type:
PublisherCopyrights
Funding Info:
Description:
© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2161-4407
2161-4393
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