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

• 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
• Keywords: DAGM 2006, Image Processing, Performance, Possible Classification Performance, Required Machine-learning Vocabulary