@article {2148, title = {A Brain Computer Interface with Online Feedback based on Magnetoencephalography.}, year = {2005}, month = {08/2005}, 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 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 {\textquotedblleft}proof of concept{\textquotedblright}.

}, keywords = {Brain Computer Interfaces, User Modelling for Computer Human Interaction}, doi = {10.1145/1102351.1102410}, url = {http://www.researchgate.net/publication/221346004_A_brain_computer_interface_with_online_feedback_based_on_magnetoencephalography}, author = {Lal, T.N and Schroeder, Michael and Jeremy Jeremy Hill and Preissl, Hubert and Hinterberger, T. and Mellinger, J{\"u}rgen and Bogdan, Martin and Rosenstiel, W. and Niels Birbaumer and Schoelkopf, Bernhard} } @article {2147, title = {Methods Towards Invasive Human Brain Computer Interfaces.}, year = {2005}, month = {2005}, abstract = {

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].\ 

}, keywords = {Brain Computer Interfaces}, isbn = {0-262-19534-8}, doi = {10.1.1.64.8486}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.8486}, author = {Lal, T.N and Hinterberger, T. and Widman, Guido and Schroeder, Michael and Jeremy Jeremy Hill and Rosenstiel, W. and Elger, Christian and Sch{\"o}lkopf, B and Niels Birbaumer} } @article {2145, title = {An Auditory Paradigm for Brain{\textendash}Computer Interfaces.}, year = {2004}, month = {2004}, abstract = {

Motivated by the particular problems involved in communicating with {\textquotedblleft}locked-in{\textquotedblright} paralysed patients, we aim to develop a brain- computer interface that uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Chan- nel Elimination on the independent components of averaged event- related potentials, we show that an untrained user{\textquoteright}s EEG data can be classified with an encouragingly high level of accuracy. This suggests that it is possible for users to modulate EEG signals in a single trial by the conscious direction of attention, well enough to be useful in BCI.\ 

}, keywords = {Brain Computer Interfaces}, url = {http://papers.nips.cc/paper/2551-an-auditory-paradigm-for-brain-computer-interfaces}, author = {Jeremy Jeremy Hill and Lal, T.N and Bierig, K. and Niels Birbaumer and Sch{\"o}lkopf, B} }