02147nas a2200145 4500008004100000245008600041210006900127260001200196520161100208653003001819100002001849700002501869700001801894856008901912 2006 eng d00aOptimizing Spatial Filters for BCI: Margin- and Evidence-Maximization Approaches.0 aOptimizing Spatial Filters for BCI Margin and EvidenceMaximizati c11/20063 a

We present easy-to-use alternatives to the often-used two-stage Common Spatial Pattern + classifier approach for spatial filtering and classification of Event-Related Desynchronization signals in BCI. We report two algorithms that aim to optimize the spatial filters according to a criterion more directly related to the ability of the algorithms to generalize to unseen data. Both are based upon the idea of treating the spatial filter coefficients as hyperparameters of a kernel or covariance function. We then optimize these hyper-parameters directly along side the normal classifier parameters with respect to our chosen learning objective function. The two objectives considered are margin maximization as used in Support-Vector Machines and the evidence maximization framework used in Gaussian Processes. Our experiments assessed generalization error as a function of the number of training points used, on 9 BCI competition data sets and 5 offline motor imagery data sets measured in Tubingen. Both our approaches sho w consistent improvements relative to the commonly used CSP+linear classifier combination. Strikingly, the improvement is most significant in the higher noise cases, when either few trails are used for training, or with the most poorly performing subjects. This a reversal of the usual "rich get richer" effect in the development of CSP extensions, which tend to perform best when the signal is strong enough to accurately find their additional parameters. This makes our approach particularly suitable for clinical application where high levels of noise are to be expected.

10aBrain Computer Interfaces1 aFarquhar, Jason1 aHill, Jeremy, Jeremy1 aSchölkopf, B uhttp://www.researchgate.net/publication/237615110_Optimizing_Spatial_Filters_for_BCI01846nas a2200241 4500008004100000245008500041210006900126260001200195520097600207653003001183653005001213100001301263700002301276700002501299700002001324700002001344700002301364700001901387700001801406700002101424700002501445856013401470 2005 eng d00aA Brain Computer Interface with Online Feedback based on Magnetoencephalography.0 aBrain Computer Interface with Online Feedback based on Magnetoen c08/20053 a

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”.

10aBrain Computer Interfaces10aUser Modelling for Computer Human Interaction1 aLal, T N1 aSchroeder, Michael1 aHill, Jeremy, Jeremy1 aPreissl, Hubert1 aHinterberger, T1 aMellinger, Jürgen1 aBogdan, Martin1 aRosenstiel, W1 aBirbaumer, Niels1 aSchoelkopf, Bernhard uhttp://www.researchgate.net/publication/221346004_A_brain_computer_interface_with_online_feedback_based_on_magnetoencephalography01807nas a2200229 4500008004100000020001800041245006200059210006100121260000900182520111100191653003001302100001301332700002001345700001801365700002301383700002501406700001801431700002101449700001801470700002101488856006801509 2005 eng d a0-262-19534-800aMethods Towards Invasive Human Brain Computer Interfaces.0 aMethods Towards Invasive Human Brain Computer Interfaces c20053 a

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]. 

10aBrain Computer Interfaces1 aLal, T N1 aHinterberger, T1 aWidman, Guido1 aSchroeder, Michael1 aHill, Jeremy, Jeremy1 aRosenstiel, W1 aElger, Christian1 aSchölkopf, B1 aBirbaumer, Niels uhttp://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.848601486nas a2200169 4500008004100000245005800041210005400099260000900153520094500162653003001107100002501137700001301162700001401175700002101189700001801210856008801228 2004 eng d00aAn Auditory Paradigm for Brain–Computer Interfaces.0 aAuditory Paradigm for Brain–Computer Interfaces c20043 a

 

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 Chan- nel 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. 

10aBrain Computer Interfaces1 aHill, Jeremy, Jeremy1 aLal, T N1 aBierig, K1 aBirbaumer, Niels1 aSchölkopf, B uhttp://papers.nips.cc/paper/2551-an-auditory-paradigm-for-brain-computer-interfaces