Detection of movement-related patterns in ongoing single-channel electrocorticogram

Adaptive autoregressive parameters and a linear classifier were used to detect movement related desynchronization and synchronization patterns in single-channel electrocorticogram (ECoG) obtained from implanted electrode grids. The best classification accuracies found had more than 90% hits and less...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2003-09, Vol.11 (3), p.276-281
Main Authors: Graimann, B., Huggins, J.E., Schlogl, A., Levine, S.P., Pfurtscheller, G.
Format: Article
Language:English
Subjects:
Biomedical engineering
Biomedical informatics
Biomedical measurements
Brain computer interfaces
Electrodes
Electroencephalography
Enterprise resource planning
Genetic algorithms
Linear discriminant analysis
Switches
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Summary:Adaptive autoregressive parameters and a linear classifier were used to detect movement related desynchronization and synchronization patterns in single-channel electrocorticogram (ECoG) obtained from implanted electrode grids. The best classification accuracies found had more than 90% hits and less than 10% false positives. The findings show that the detection of event-related desynchronization and synchronization in ECoG data can be used to reliably provide switch control directly by the brain and is therefore very suitable as the basis of a direct brain interface.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2003.816863