A Brain Computer Interface with Online Feedback based on Magnetoencephalography.

TitleA Brain Computer Interface with Online Feedback based on Magnetoencephalography.
Publication TypeJournal Article
Year of Publication2005
AuthorsLal, TN, Schroeder, M, Jeremy Jeremy Hill, Preissl, H, Hinterberger, T, Mellinger, J, Bogdan, M, Rosenstiel, W, Birbaumer, N, Schoelkopf, B
Date Published08/2005
KeywordsBrain Computer Interfaces, User Modelling for Computer Human Interaction

The aim of this paper is to show that machine learning techniques can be used to derive a classifying function for human brain signal data measured by magnetoencephalography (MEG), for the use in a brain computer interface (BCI). This is especially helpful for evaluating quickly whether a BCI approach based on electroencephalography, on which training may be slower due to lower signalto-noise ratio, is likely to succeed. We apply RCE and regularized SVMs to the experimental data of ten healthy subjects performing a motor imagery task. Four subjects were able to use a trained classifier to write a short name. Further analysis gives evidence that the proposed imagination task is suboptimal for the possible extension to a multiclass interface. To the best of our knowledge this paper is the first working online MEG-based BCI and is therefore a “proof of concept”.


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