Please use this identifier to cite or link to this item: https://doi.org/10.1109/EMBC.2013.6609969
Title: Improving session-to-session transfer performance of motor imagery-based BCI using adaptive extreme learning machine
Authors: Bamdadian, A.
Guan, C.
Ang, K.K.
Xu, J. 
Issue Date: 2013
Citation: Bamdadian, A.,Guan, C.,Ang, K.K.,Xu, J. (2013). Improving session-to-session transfer performance of motor imagery-based BCI using adaptive extreme learning machine. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS : 2188-2191. ScholarBank@NUS Repository. https://doi.org/10.1109/EMBC.2013.6609969
Abstract: Non-stationarity of electroencephalograph (EEG) data from session-to-session transfer is one of the challenges for EEG-based brain-computer interface systems, which can inversely affect their performance. Among methods proposed to address non-stationarity, adaptation is a promising method. In this study, an adaptive extreme learning machine (AELM) is proposed to update the initial classifier from the calibration session by using chunks of EEG data from the evaluation session whereby the common spatial pattern (CSP) algorithm is used to extract the most discriminative features. The effectiveness of the proposed algorithm is on motor imagery data collected from 12 healthy subjects during a calibration session and an evaluation session on a separate day. The results from the proposed AELM were compared with non-adaptive ELM and SVM classifiers. The results showed that AELM was significantly better (p=0.03). Moreover, the results also showed that accumulating the evaluation session data and useing them for adapting the classifier will significantly improve the performance (p=0.001). Hence, the proposed AELM is effective in addressing the non-stationarity of EEG signal for online BCI systems. © 2013 IEEE.
Source Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
URI: http://scholarbank.nus.edu.sg/handle/10635/83837
ISBN: 9781457702167
ISSN: 1557170X
DOI: 10.1109/EMBC.2013.6609969
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