Datensatz

Zusammenfassung

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”.