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DAGM
2006
Springer

Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals

10 years 6 months ago
Classifying Event-Related Desynchronization in EEG, ECoG and MEG Signals
We present the results from three motor-imagery-based Brain-Computer Interface experiments. Brain signals were recorded from 8 untrained subjects using EEG, 4 using ECoG and 10 using MEG. In all cases, we aim to develop a system that could be used for fast, reliable preliminary screening in the clinical application of a BCI, so we aim to obtain the best possible classification performance in a short time. Accordingly, the burden of adaptation is on the side of the computer rather than the user, so we must adopt a machine-learning approach to the analysis. We introduce the required machine-learning vocabulary and concepts, and then present quantitative results that focus on two main issues. The first is the effect of the number of trials--how long does the recording session need to be? We find that good performance could be achieved, on average, after the first 200 trials in EEG, 75
N. Jeremy Hill, Thomas Navin Lal, Michael Schr&oum
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where DAGM
Authors N. Jeremy Hill, Thomas Navin Lal, Michael Schröder 0002, Thilo Hinterberger, Guido Widman, Christian Erich Elger, Bernhard Schölkopf, Niels Birbaumer
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