H Raza
Toward transductive learning classifiers for non-stationary EEG
Raza, H; Prasad, G; Li, Y; Cecotti, H
Authors
G Prasad
Y Li
H Cecotti
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.
Citation
Raza, H., Prasad, G., Li, Y., & Cecotti, H. (2014, August). Toward transductive learning classifiers for non-stationary EEG. Presented at 36th Annual International Conference of the Institute of Electrical and Electronics Engineers (IEEE) Engineering in Medicine and Biology Society, Chicago, Illinois, USA
Presentation Conference Type | Other |
---|---|
Conference Name | 36th Annual International Conference of the Institute of Electrical and Electronics Engineers (IEEE) Engineering in Medicine and Biology Society |
Conference Location | Chicago, Illinois, USA |
Start Date | Aug 26, 2014 |
End Date | Aug 30, 2014 |
Publication Date | Aug 28, 2014 |
Deposit Date | Jun 19, 2015 |
Publisher | Institute of Electrical and Electronics Engineers |
Additional Information | Event Type : Conference |
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