<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lal, T.N</style></author><author><style face="normal" font="default" size="100%">Schroeder, Michael</style></author><author><style face="normal" font="default" size="100%">Jeremy Jeremy Hill</style></author><author><style face="normal" font="default" size="100%">Preissl, Hubert</style></author><author><style face="normal" font="default" size="100%">Hinterberger, T.</style></author><author><style face="normal" font="default" size="100%">Mellinger, Jürgen</style></author><author><style face="normal" font="default" size="100%">Bogdan, Martin</style></author><author><style face="normal" font="default" size="100%">Rosenstiel, W.</style></author><author><style face="normal" font="default" size="100%">Niels Birbaumer</style></author><author><style face="normal" font="default" size="100%">Schoelkopf, Bernhard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Brain Computer Interface with Online Feedback based on Magnetoencephalography.</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Brain Computer Interfaces</style></keyword><keyword><style  face="normal" font="default" size="100%">User Modelling for Computer Human Interaction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2005</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.researchgate.net/publication/221346004_A_brain_computer_interface_with_online_feedback_based_on_magnetoencephalography</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-family: arial, sans-serif; font-size: 12px; line-height: 16px;&quot;&gt;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”.&lt;/span&gt;&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schröder, Michael</style></author><author><style face="normal" font="default" size="100%">Lal, T.N</style></author><author><style face="normal" font="default" size="100%">Hinterberger, T.</style></author><author><style face="normal" font="default" size="100%">Bogdan, Martin</style></author><author><style face="normal" font="default" size="100%">Jeremy Jeremy Hill</style></author><author><style face="normal" font="default" size="100%">Niels Birbaumer</style></author><author><style face="normal" font="default" size="100%">Rosenstiel, W.</style></author><author><style face="normal" font="default" size="100%">Schölkopf, B</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Vesin J M, T EbrahimiEditor</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces.</style></title><secondary-title><style face="normal" font="default" size="100%">EURASIP Journal on Advances in Signal Processing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">channel selection</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">feature selection</style></keyword><keyword><style  face="normal" font="default" size="100%">recursive channel elimination</style></keyword><keyword><style  face="normal" font="default" size="100%">support vector machine</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2005</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.researchgate.net/publication/26532072_Robust_EEG_Channel_Selection_across_Subjects_for_Brain-Computer_Interfaces</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2005</style></volume><pages><style face="normal" font="default" size="100%">3103–3112</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;em&gt;&lt;br /&gt;&lt;/em&gt;&lt;/p&gt;</style></abstract></record></records></xml>