@article {3391, title = {Word-level language modeling for P300 spellers based on discriminative graphical models.}, journal = {J Neural Eng}, volume = {12}, year = {2015}, month = {04/2015}, pages = {026007}, abstract = {

OBJECTIVE: In this work we propose a probabilistic graphical model framework that uses language priors at the level of words as a mechanism to increase the performance of P300-based spellers.

APPROACH: This paper is concerned with brain-computer interfaces based on P300 spellers. Motivated by P300 spelling scenarios involving communication based on a limited vocabulary, we propose a probabilistic graphical model framework and an associated classification algorithm that uses learned statistical models of language at the level of words. Exploiting such high-level contextual information helps reduce the error rate of the speller.

MAIN RESULTS: Our experimental results demonstrate that the proposed approach offers several advantages over existing methods. Most importantly, it increases the classification accuracy while reducing the number of times the letters need to be flashed, increasing the communication rate of the system.

SIGNIFICANCE: The proposed approach models all the variables in the P300 speller in a unified framework and has the capability to correct errors in previous letters in a word, given the data for the current one. The structure of the model we propose allows the use of efficient inference algorithms, which in turn makes it possible to use this approach in real-time applications.

}, keywords = {Brain Computer Interfaces, inference algorithms, language models, P300 speller, probabilistic graphical models}, issn = {1741-2552}, doi = {10.1088/1741-2560/12/2/026007}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25686293}, author = {Saa, Jaime F Delgado and Pesters, Adriana de and Dennis J. McFarland and {\c C}etin, M{\"u}jdat} } @article {2142, title = {Optimizing Spatial Filters for BCI: Margin- and Evidence-Maximization Approaches.}, year = {2006}, month = {11/2006}, abstract = {

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.

}, keywords = {Brain Computer Interfaces}, url = {http://www.researchgate.net/publication/237615110_Optimizing_Spatial_Filters_for_BCI}, author = {Farquhar, Jason and Jeremy Jeremy Hill and Sch{\"o}lkopf, B} } @article {2148, title = {A Brain Computer Interface with Online Feedback based on Magnetoencephalography.}, year = {2005}, month = {08/2005}, abstract = {

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 {\textquotedblleft}proof of concept{\textquotedblright}.

}, keywords = {Brain Computer Interfaces, User Modelling for Computer Human Interaction}, doi = {10.1145/1102351.1102410}, url = {http://www.researchgate.net/publication/221346004_A_brain_computer_interface_with_online_feedback_based_on_magnetoencephalography}, author = {Lal, T.N and Schroeder, Michael and Jeremy Jeremy Hill and Preissl, Hubert and Hinterberger, T. and Mellinger, J{\"u}rgen and Bogdan, Martin and Rosenstiel, W. and Niels Birbaumer and Schoelkopf, Bernhard} } @article {2147, title = {Methods Towards Invasive Human Brain Computer Interfaces.}, year = {2005}, month = {2005}, abstract = {

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].\ 

}, keywords = {Brain Computer Interfaces}, isbn = {0-262-19534-8}, doi = {10.1.1.64.8486}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.64.8486}, author = {Lal, T.N and Hinterberger, T. and Widman, Guido and Schroeder, Michael and Jeremy Jeremy Hill and Rosenstiel, W. and Elger, Christian and Sch{\"o}lkopf, B and Niels Birbaumer} } @conference {3436, title = {Tracking of the mu rhythm using an empirically derived matched filter.}, booktitle = {Proc. IEEE International Conference of Neural Engineering}, year = {2005}, month = {03/2005}, publisher = {IEEE}, organization = {IEEE}, address = {Arlington, VA}, keywords = {bioelectric potentials, Brain Computer Interfaces, brain modeling, brain-computer interface, communication device, communication system control, cortical mu rhythm modulation, EEG, Electroencephalography, empirically derived matched filter, handicapped aids, laboratories, matched filters, medical signal detection, medical signal processing, monitoring, motor imagery, mu rhythm tracking, noninvasive treatment, rhythm, synchronous motors, two-dimensional cursor control data}, isbn = {0-7803-8710-4}, doi = {10.1109/CNE.2005.1419559}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1419559}, author = {Krusienski, Dean J and Gerwin Schalk and Dennis J. McFarland and Jonathan Wolpaw} } @article {2145, title = {An Auditory Paradigm for Brain{\textendash}Computer Interfaces.}, year = {2004}, month = {2004}, abstract = {

Motivated by the particular problems involved in communicating with {\textquotedblleft}locked-in{\textquotedblright} 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{\textquoteright}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.\ 

}, keywords = {Brain Computer Interfaces}, url = {http://papers.nips.cc/paper/2551-an-auditory-paradigm-for-brain-computer-interfaces}, author = {Jeremy Jeremy Hill and Lal, T.N and Bierig, K. and Niels Birbaumer and Sch{\"o}lkopf, B} }