%0 Journal Article %D 2005 %T A Brain Computer Interface with Online Feedback based on Magnetoencephalography. %A Lal, T.N %A Schroeder, Michael %A Jeremy Jeremy Hill %A Preissl, Hubert %A Hinterberger, T. %A Mellinger, Jürgen %A Bogdan, Martin %A Rosenstiel, W. %A Niels Birbaumer %A Schoelkopf, Bernhard %K Brain Computer Interfaces %K User Modelling for Computer Human Interaction %X

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”.

%8 08/2005 %G eng %U http://www.researchgate.net/publication/221346004_A_brain_computer_interface_with_online_feedback_based_on_magnetoencephalography %R 10.1145/1102351.1102410 %0 Journal Article %D 2005 %T Methods Towards Invasive Human Brain Computer Interfaces. %A Lal, T.N %A Hinterberger, T. %A Widman, Guido %A Schroeder, Michael %A Jeremy Jeremy Hill %A Rosenstiel, W. %A Elger, Christian %A Schölkopf, B %A Niels Birbaumer %K Brain Computer Interfaces %X

During the last ten years there has been growing interest in the develop- ment of Brain Computer Interfaces (BCIs). The field has mainly been driven by the needs of completely paralyzed patients to communicate. With a few exceptions, most human BCIs are based on extracranial elec- troencephalography (EEG). However, reported bit rates are still low. One reason for this is the low signal-to-noise ratio of the EEG [16]. We are currently investigating if BCIs based on electrocorticography (ECoG) are a viable alternative. In this paper we present the method and examples of intracranial EEG recordings of three epilepsy patients with electrode grids placed on the motor cortex. The patients were asked to repeat- edly imagine movements of two kinds, e.g., tongue or finger movements. We analyze the classifiability of the data using Support Vector Machines (SVMs) [18, 21] and Recursive Channel Elimination (RCE) [11]. 

%8 2005 %@ 0-262-19534-8 %G eng %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.8486 %R 10.1.1.64.8486