Automated labeling of electroencephalography data using quasi-supervised learning | IEEE Conference Publication | IEEE Xplore

Automated labeling of electroencephalography data using quasi-supervised learning


Abstract:

In this study, the separation of the stimulus effects from the baseline was investigated in electroencephalography data recorded under different visual stimuli using quas...Show More

Abstract:

In this study, the separation of the stimulus effects from the baseline was investigated in electroencephalography data recorded under different visual stimuli using quasi-supervised learning. The data feature vectors were constructed using independent component analysis and wavelet transform, and then, these feature vectors were separated using quasi-supervised learning. Experiment results showed that the EEG data of the stimuli can be separated using quasi-supervised learning.
Date of Conference: 18-20 April 2012
Date Added to IEEE Xplore: 28 May 2012
ISBN Information:
Print ISSN: 2165-0608
Conference Location: Mugla, Turkey

References

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