|Title||A Brain Computer Interface with Online Feedback based on Magnetoencephalography |
|Publication Type||Journal Article |
|Year of Publication||2005 |
|Authors||Lal, TN, Schroeder, M, Hill, N Jeremy, Preissl, H, Hinterberger, T, Mellinger, J, Bogdan, M, Rosenstiel, W, Birbaumer, N, Schoelkopf, B |
|Keywords||Brain 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”.