A Brain-Computer Interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user’s brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. This article describes the data sets that were provided to the competitors and gives an overview of the results. In a series of accompanying articles, the winning teams describe their methods in detail.

We summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of EEG or ECoG signals for each subject. We apply the same experimental and analytical methods to 11 non-paralysed subjects (8 EEG, 3 ECoG), and to 5 paralysed subjects (4 EEG, 1 ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the non-paralysed subjects, it proved impossible to classify the signals obtained from the paralysed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

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”.

Abstract—This paper studies classifiability of electrocorticographic signals (ECoG) for use in a human brain–computer interface (BCI). The results show that certain spectral features can be reliably used across several subjects to accurately classify different types of movements. Sparse and nonsparse versions of the support vector machine and regularized linear discriminant analysis linear classifiers are assessed and contrasted for the classification problem. In conjunction with a careful choice of features, the classification process automatically and consistently identifies neurophysiological areas known to be involved in the movements. An average two-class classification accuracy of 95% for real movement and around 80 % for imagined movement is shown. The high accuracy and generalizability of these results, obtained with as few as 30 data samples per class, support the use of classification methods for ECoG-based BCIs. Index Terms—Brain–computer interfaces, classification, electrocorticography, feature selection, neural interfaces.

For ease of reference, we restate the key definitions and assumptions from the main paper. Definition 1 (Uniform Convergence). Loss functionLhasǫ-uniform convergence if with probability

The thesis examines normality and temporal correlation of samples in EEG data. Pre-sented in the context of Bayesian classification, empirical results quantify the advantages of modeling the temporal information. A connection to the theoretical background of the underlying classifiers is also discussed. Using a five-task six-channel dataset, the ex-periments demonstrate that an increase in performance can be observed by simple con-sideration of multiple samples. Exploitation of temporal structure leads to additional improvement, but not always. The measurement of Normality is used to demonstrate an alternative to cross-validation, where each class is considered independently of the others.

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Abstract—This paper studies classifiability of electrocorticographic signals (ECoG) for use in a human Brain-Computer Interface (BCI). The results show that certain spectral features can be reliably used across several subjects to accurately classify different types of movements. Sparse and non-sparse versions of the SVM and RLDA linear classifiers are assessed and contrasted for the classification problem. In conjunction with a careful choice of features, the classification process automatically and consistently identifies neurophysiological areas known to be involved in the movements. An average 2-class classification accuracy of 95% for real movement and around 80 % for imagined movement is shown. The high accuracy and generalizability of these results, obtained with as few as 30 data samples per class, support the use of classification methods for ECoG-based BCIs. I.