Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring

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Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring
Authors: Klaus-Robert Müller, Michael Tangermann, Guido Dornhege, Matthias Krauledat, Gabriel Curio, Benjamin Blankertz
Citation: Journal of Neuroscience Methods 167 (1): 82-90. 2008 January
Database(s): Google Scholar cites PubMed (PMID/18031824)
DOI: 10.1016/j.jneumeth.2007.09.022.
Link(s): http://uais.lzu.edu.cn/uploads/soft/101110/1-101110222928.pdf
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Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring reviews experiments with brain-computer interface and mental state monitoring with electroencephalography.

[edit] Method

[edit] Mental state monitoring

For mental state monitoring arousal is regarded by monitoring a subject performing a monotonuous task. The varying degree of errors the subject made through time was smoothed to an error index and then regarded as a proxy for "concentration". EEG trials were labeled into two classes depending on the error index, and the labels were then used in a linear discriminant analysis classifier with the processed EEG data as features. The processing used a filter for the alpha band and computed power features for 2 seconds windows for each of the 128 electrodes, - except for those electrodes excluded dues to base impedance.

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