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Toward transductive learning classifiers for non-stationary EEG

Raza, H, Prasad, G, Li, Y and Cecotti, H 2014, Toward transductive learning classifiers for non-stationary EEG , in: 36th Annual International Conference of the Institute of Electrical and Electronics Engineers (IEEE) Engineering in Medicine and Biology Society, 26-30 August 2014, Chicago, Illinois, USA.

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Abstract

A major challenge in brain-computer interfaces (BCIs) based on electroencephalogram (EEG) signals is the immanent non-stationarities in EEG data. Statistical properties of the signals may shift from inter-or-intra session transfer that often led to deteriorated BCI performance. We propose to handle the issue with a transductive learning approach. The performance of the proposed method is evaluated on BCI competition 2008-Graz dataset B. The results show an improvement in classification accuracy over the traditional learning method.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 19 Jun 2015 18:27
Last Modified: 05 Apr 2016 18:18
URI: http://usir.salford.ac.uk/id/eprint/33102

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