Lal, T., Schröder, M., Hill, J., Preissl, H., Hinterberger, T., Mellinger, J., Bogdan, M., Rosenstiel, W., Hofmann, T., Birbaumer, N., Schölkopf, B.
In Proceedings of the 22nd International Conference on Machine Learning, pages: 465-472, (Editors: L De Raedt and S Wrobel), ACM, New York, NY, USA, ICML, 2005 (inproceedings)
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 recursive channel elimination 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 together with a decision tree interface 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 BCI based on MEG recordings and is therefore a proof of concept.
Schröder, M., Lal, T., Hinterberger, T., Bogdan, M., Hill, J., Birbaumer, N., Rosenstiel, W., Schölkopf, B.
EURASIP Journal on Applied Signal Processing, 2005(19, Special Issue: Trends in Brain Computer Interfaces):3103-3112, (Editors: Vesin, J. M., T. Ebrahimi), 2005 (article)
Most EEG-based Brain Computer Interface (BCI)
paradigms come along with specific electrode positions, e.g.~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 showed that recording positions can
be found without the use of prior knowledge about the paradigm used. However it remains unclear to what extend 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.
We explored several ways to improve the efficiency of measuring psychometric functions without resorting to adaptive procedures. a) The number m of alternatives in an m-alternative-forced-choice (m-AFC) task improves the efficiency of the method of constant stimuli. b) When alternatives are presented simultaneously on different positions on a screen rather than sequentially time can be saved and memory load for the subject can be reduced. c) A touch-screen can further help to make the experimental procedure more intuitive. We tested these ideas in the measurement of contrast sensitivity and compared them to results obtained by sequential presentation in two-interval-forced-choice (2-IFC). Qualitatively all methods (m-AFC and 2-IFC) recovered the characterictic shape of the contrast sensitivity function in three subjects. The m-AFC paradigm only took about 60% of the time of the 2-IFC task. We tried m=2,4,8 and found 4-AFC to give the best model fits and 2-AFC to have the least bias.
In BioCAS04, (S3/5/INV- S3/17-20):4, IEEE Computer Society, Los Alamitos, CA, USA, 2004 IEEE International Workshop on Biomedical Circuits and Systems, December 2004 (inproceedings)
Motivated by the particular problems involved in communicating with "locked-in" 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 Channel Elimination on the independent components of averaged event-related potentials, we show that an untrained user‘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.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems