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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

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".

We present the results from three motor-imagery-based Brain-Computer Interface experiments. Brain signals were recorded from 8 untrained subjects using EEG, 4 using ECoG and 10 using MEG. In all cases, we aim to develop a system that could be used for fast, reliable preliminary screening in the clinical application of a BCI, so we aim to obtain the best possible classification performance in a short time. Accordingly, the burden of adaptation is on the side of the computer rather than the user, so we must adopt a machine-learning approach to the analysis. We introduce the required machine-learning vocabulary and concepts, and then present quantitative results that focus on two main issues. The first is the effect of the number of trials-how long does the recording session need to be? We find that good performance could be achieved, on average, after the first 200 trials in EEG, 75-100 trials in MEG, or 25-50 trials in ECoG. The second issue is the effect of spatial filtering-we compare the performance of the original sensor signals with that of the outputs of Independent Component Analysis and the Common Spatial Pattern algorithm, in each of the three sensor types. We find that spatial filtering does not help in MEG, helps a little in ECoG, and improves performance a great deal in EEG. The unsupervised ICA algorithm performed at least as well as the supervised CSP algorithm in all cases-the latter suffered from poor generalization performance due to overfitting in ECoG and MEG, although this could be alleviated by reducing the number of sensors used as input to the algorithm.

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