TY - JOUR T1 - An MEG-based brain-computer interface (BCI). JF - Neuroimage Y1 - 2007 A1 - Mellinger, Jürgen A1 - Gerwin Schalk A1 - Christoph Braun A1 - Preissl, Hubert A1 - Rosenstiel, W. A1 - Niels Birbaumer A1 - Kübler, A. KW - Adult KW - Algorithms KW - Artifacts KW - Brain KW - Electroencephalography KW - Electromagnetic Fields KW - Electromyography KW - Feedback KW - Female KW - Foot KW - Hand KW - Head Movements KW - Humans KW - Magnetic Resonance Imaging KW - Magnetoencephalography KW - Male KW - Movement KW - Principal Component Analysis KW - Signal Processing, Computer-Assisted KW - User-Computer Interface AB -

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography(EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

VL - 36 UR - http://www.ncbi.nlm.nih.gov/pubmed/17475511 IS - 3 ER - TY - JOUR T1 - A Brain Computer Interface with Online Feedback based on Magnetoencephalography. Y1 - 2005 A1 - Lal, T.N A1 - Schroeder, Michael A1 - Jeremy Jeremy Hill A1 - Preissl, Hubert A1 - Hinterberger, T. A1 - Mellinger, Jürgen A1 - Bogdan, Martin A1 - Rosenstiel, W. A1 - Niels Birbaumer A1 - Schoelkopf, Bernhard KW - Brain Computer Interfaces KW - User Modelling for Computer Human Interaction AB -

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”.

UR - http://www.researchgate.net/publication/221346004_A_brain_computer_interface_with_online_feedback_based_on_magnetoencephalography ER -