Please use this identifier to cite or link to this item: https://doi.org/10.1109/IJCNN.2012.6252652
Title: Dynamically weighted classification with clustering to tackle non-stationarity in Brain computer interfacing
Authors: Liyanage, S.R.
Guan, C.
Zhang, H.
Ang, K.K.
Xu, J.-X. 
Lee, T.H. 
Keywords: Brain-computer interface (BCI)
classification
clustering
motor imagery
Issue Date: 2012
Source: Liyanage, S.R.,Guan, C.,Zhang, H.,Ang, K.K.,Xu, J.-X.,Lee, T.H. (2012). Dynamically weighted classification with clustering to tackle non-stationarity in Brain computer interfacing. Proceedings of the International Joint Conference on Neural Networks : -. ScholarBank@NUS Repository. https://doi.org/10.1109/IJCNN.2012.6252652
Abstract: This paper addresses an important problem known as EEG non-stationarity in Brain-computer Interfacing. We propose a novel technique called Dynamically Weighted Classification with Clustering (DWCC), which explores hidden states in non-stationary EEG using a modified k-means clustering method by combining cosine distance measure and mutual information criterion. DWCC builds a set of classifiers, one for each pair of clusters from different classes. A dynamically-weighted classifier ensemble network is trained to combine the outputs of the classifiers, where we propose to dynamically assign the weight of a classifier for each test sample based on its distances to the cluster centres associated with the classifier. Experimental results on publicly available BCI Competition IV Dataset 2a yielded a mean accuracy of 81.5% which is statistically significant (t-test p<60;0.05) compared to the baseline result of 75.9% using a single classifier. © 2012 IEEE.
Source Title: Proceedings of the International Joint Conference on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/70058
ISBN: 9781467314909
DOI: 10.1109/IJCNN.2012.6252652
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