|A Brain Computer Interface with Online Feedback based on Magnetoencephalography.
|Year of Publication
|Lal, TN, Schroeder, M, Jeremy Jeremy Hill, Preissl, H, Hinterberger, T, Mellinger, J, Bogdan, M, Rosenstiel, W, Birbaumer, N, Schoelkopf, B
|Brain 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”.