Faster P300 Classifier Training Using Spatiotemporal Beamforming

The linearly-constrained minimum-variance (LCMV) beamformer is traditionally used as a spatial filter for source localization, but here we consider its spatiotemporal extension for P300 classification. We compare two variants and show that the spatiotemporal LCMV beamformer is at par with state-of-the-art P300 classifiers, but several orders of magnitude faster in training the classifier.

[1]  Wei Wu,et al.  An Idle-State Detection Algorithm for SSVEP-Based Brain-Computer Interfaces Using a Maximum Evoked Response Spatial Filter , 2015, Int. J. Neural Syst..

[2]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[3]  B. Blankertz,et al.  (C)overt attention and visual speller design in an ERP-based brain-computer interface , 2010, Behavioral and Brain Functions.

[4]  R. Barry,et al.  Removal of ocular artifact from the EEG: a review , 2000, Neurophysiologie Clinique/Clinical Neurophysiology.

[5]  Moritz Grosse-Wentrup,et al.  Beamforming in Noninvasive Brain–Computer Interfaces , 2009, IEEE Transactions on Biomedical Engineering.

[6]  J. Buford,et al.  Brain–Computer Interface after Nervous System Injury , 2014, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[7]  S. Sathiya Keerthi,et al.  A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs , 2005, J. Mach. Learn. Res..

[8]  J.J. Vidal,et al.  Real-time detection of brain events in EEG , 1977, Proceedings of the IEEE.

[9]  Salil H. Patel,et al.  Characterization of N200 and P300: Selected Studies of the Event-Related Potential , 2005, International journal of medical sciences.

[10]  C. A. Grimbergen,et al.  HIGH QUALITY RECORDING OF BIOELECTRIC EVENTS . I : INTERFERENCE REDUCTION , THEORY AND PRACTICE , 2009 .

[11]  J. W. Minett,et al.  Optimizing the P300-based brain–computer interface: current status, limitations and future directions , 2011, Journal of neural engineering.

[12]  Xingyu Wang,et al.  An ERP-Based BCI using an oddball Paradigm with Different Faces and Reduced errors in Critical Functions , 2014, Int. J. Neural Syst..

[13]  Steven Laureys,et al.  A Comparison of Two Spelling Brain-Computer Interfaces Based on Visual P3 and SSVEP in Locked-In Syndrome , 2013, PloS one.

[14]  Hubert Cecotti,et al.  Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Klaus-Robert Müller,et al.  Incremental Support Vector Learning: Analysis, Implementation and Applications , 2006, J. Mach. Learn. Res..

[16]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[17]  S J Luck,et al.  Visual event-related potentials index focused attention within bilateral stimulus arrays. II. Functional dissociation of P1 and N1 components. , 1990, Electroencephalography and clinical neurophysiology.

[18]  E. W. Sellers,et al.  Toward enhanced P300 speller performance , 2008, Journal of Neuroscience Methods.

[19]  Marc M. Van Hulle,et al.  Single-Trial ERP Component Analysis Using a Spatiotemporal LCMV Beamformer , 2016, IEEE Transactions on Biomedical Engineering.

[20]  Dean J Krusienski,et al.  A comparison of classification techniques for the P300 Speller , 2006, Journal of neural engineering.

[21]  Urbano Nunes,et al.  Statistical spatial filtering for a P300-based BCI: Tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis , 2011, Journal of Neuroscience Methods.

[22]  Stefan Haufe,et al.  On the interpretation of weight vectors of linear models in multivariate neuroimaging , 2014, NeuroImage.

[23]  Stefan Haufe,et al.  Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methods. , 2013, Journal of neural engineering.

[24]  Xingyu Wang,et al.  A P300 Brain-Computer Interface Based on a Modification of the Mismatch Negativity Paradigm , 2015, Int. J. Neural Syst..

[25]  A. Kübler,et al.  Flashing characters with famous faces improves ERP-based brain–computer interface performance , 2011, Journal of neural engineering.

[26]  Tom Chau,et al.  Electrode Fusion for the Prediction of Self-Initiated Fine Movements from Single-Trial Readiness Potentials , 2015, Int. J. Neural Syst..

[27]  Tzyy-Ping Jung,et al.  A High-Speed Brain Speller using steady-State Visual evoked potentials , 2014, Int. J. Neural Syst..

[28]  Xingyu Wang,et al.  Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis , 2013, Int. J. Neural Syst..

[29]  Arne Robben,et al.  Towards the detection of error-related potentials and its integration in the context of a P300 speller brain-computer interface , 2012, Neurocomputing.

[30]  N. Birbaumer,et al.  Brain–computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralysed patients? , 2008, Clinical Neurophysiology.

[31]  J J Vidal,et al.  Toward direct brain-computer communication. , 1973, Annual review of biophysics and bioengineering.

[32]  R. Knight,et al.  Neural origins of the P300. , 2000, Critical reviews in neurobiology.

[33]  H. Adeli,et al.  Brain-computer interface technologies: from signal to action , 2013, Reviews in the neurosciences.

[34]  Guillaume Gibert,et al.  xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface , 2009, IEEE Transactions on Biomedical Engineering.

[35]  J. Polich,et al.  Cognitive and biological determinants of P300: an integrative review , 1995, Biological Psychology.

[36]  Gernot R. Müller-Putz,et al.  A Single-Switch BCI Based on Passive and imagined movements: toward Restoring Communication in Minimally Conscious patients , 2013, Int. J. Neural Syst..

[37]  J. Polich Updating P300: An integrative theory of P3a and P3b , 2007, Clinical Neurophysiology.

[38]  W. Drongelen,et al.  Localization of brain electrical activity via linearly constrained minimum variance spatial filtering , 1997, IEEE Transactions on Biomedical Engineering.

[39]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[40]  Francisco J. Pelayo,et al.  An auditory Brain-Computer Interface with Accuracy Prediction , 2012, Int. J. Neural Syst..

[41]  Xingyu Wang,et al.  A combined brain–computer interface based on P300 potentials and motion-onset visual evoked potentials , 2012, Journal of Neuroscience Methods.

[42]  Chang S. Nam,et al.  Effects of Luminosity Contrast and Stimulus Duration on User Performance and Preference in a P300-Based Brain–Computer Interface , 2014, Int. J. Hum. Comput. Interact..

[43]  A. Kok On the utility of P3 amplitude as a measure of processing capacity. , 2001, Psychophysiology.

[44]  N. Birbaumer,et al.  Predictability of Brain-Computer Communication , 2004 .

[45]  C Neuper,et al.  A comparison of three brain–computer interfaces based on event-related desynchronization, steady state visual evoked potentials, or a hybrid approach using both signals , 2011, Journal of neural engineering.