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, Hill, N Jeremy, Preissl, H, Hinterberger, T, Mellinger, J, Bogdan, M, Rosenstiel, W, Birbaumer, N, Schoelkopf, B
KeywordsBrain Computer Interfaces, User Modelling for Computer Human Interaction
Abstract 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 recursive channel elimination 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 together with a decision tree interface 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 BCI based on MEG recordings and is therefore a “proof of concept”.
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