01846nas a2200241 4500008004100000245008500041210006900126260001200195520097600207653003001183653005001213100001301263700002301276700002501299700002001324700002001344700002301364700001901387700001801406700002101424700002501445856013401470 2005 eng d00aA Brain Computer Interface with Online Feedback based on Magnetoencephalography.0 aBrain Computer Interface with Online Feedback based on Magnetoen c08/20053 a

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”.

10aBrain Computer Interfaces10aUser Modelling for Computer Human Interaction1 aLal, T N1 aSchroeder, Michael1 aHill, Jeremy, Jeremy1 aPreissl, Hubert1 aHinterberger, T1 aMellinger, Jürgen1 aBogdan, Martin1 aRosenstiel, W1 aBirbaumer, Niels1 aSchoelkopf, Bernhard uhttp://www.researchgate.net/publication/221346004_A_brain_computer_interface_with_online_feedback_based_on_magnetoencephalography02144nas a2200301 4500008004100000245008000041210006900121260001200190300001600202490000900218520113600227653002901363653002201392653002701414653002201441653003401463653002701497100002301524700001301547700002001560700001901580700002501599700002101624700001801645700001801663700003301681856012801714 2005 eng d00aRobust EEG Channel Selection across Subjects for Brain-Computer Interfaces.0 aRobust EEG Channel Selection across Subjects for BrainComputer I c01/2005 a3103–31120 v20053 a

Most EEG-based brain-computer interface (BCI) paradigms come along with specific electrode positions, for example, for a visual-based BCI, electrode positions close to the primary visual cortex are used. For new BCI paradigms it is usually not known where task relevant activity can be measured from the scalp. For individual subjects, Lal et al. in 2004 showed that recording positions can be found without the use of prior knowledge about the paradigm used. However it remains unclear to what extent their method of recursive channel elimination (RCE) can be generalized across subjects. In this paper we transfer channel rankings from a group of subjects to a new subject. For motor imagery tasks the results are promising, although cross-subject channel selection does not quite achieve the performance of channel selection on data of single subjects. Although the RCE method was not provided with prior knowledge about the mental task, channels that are well known to be important (from a physiological point of view) were consistently selected whereas task-irrelevant channels were reliably disregarded.

10abrain-computer interface10achannel selection10aElectroencephalography10afeature selection10arecursive channel elimination10asupport vector machine1 aSchröder, Michael1 aLal, T N1 aHinterberger, T1 aBogdan, Martin1 aHill, Jeremy, Jeremy1 aBirbaumer, Niels1 aRosenstiel, W1 aSchölkopf, B1 aVesin J M, EbrahimiEditor, T uhttp://www.researchgate.net/publication/26532072_Robust_EEG_Channel_Selection_across_Subjects_for_Brain-Computer_Interfaces