Brain-Computer Interfaces and Visual Activity

There is growing interest in the use of brain signals for communication and operation of devices – in particular, for physically disabled people. Brain states can be detected and translated into actions such as selecting a letter from a virtual keyboard, playing a video game, or moving a robot arm. This chap‐ ter presents what is known about the effects of visual stimuli on brain activity and introduces means of monitoring brain activity. Possibilities of brain‐controlled interfaces, either with the brain signals as the sole input or in combination with the measured point of gaze, are discussed.

[1]  L. R. Quitadamo,et al.  Which Physiological Components are More Suitable for Visual ERP Based Brain–Computer Interface? A Preliminary MEG/EEG Study , 2010, Brain Topography.

[2]  Wolfgang Rosenstiel,et al.  An MEG-based brain–computer interface (BCI) , 2007, NeuroImage.

[3]  H. Flor,et al.  A multimodal brain-based feedback and communication system , 2004, Experimental Brain Research.

[4]  Sven-Thomas Graupner,et al.  Surprise, surprise: two distinct components in the visually evoked distractor effect. , 2007, Psychophysiology.

[5]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[6]  B. Blankertz,et al.  A New Auditory Multi-Class Brain-Computer Interface Paradigm: Spatial Hearing as an Informative Cue , 2010, PloS one.

[7]  José del R. Millán,et al.  Noninvasive brain-actuated control of a mobile robot by human EEG , 2004, IEEE Transactions on Biomedical Engineering.

[8]  G. Pfurtscheller,et al.  Motor imagery activates primary sensorimotor area in humans , 1997, Neuroscience Letters.

[9]  Klaus-Robert Müller,et al.  Playing Pinball with non-invasive BCI , 2008, NIPS.

[10]  Jukka Heikkonen,et al.  A local neural classifier for the recognition of EEG patterns associated to mental tasks , 2002, IEEE Trans. Neural Networks.

[11]  E. Lalor,et al.  A comparison of covert and overt attention as a control option in a steady-state visual evoked potential-based brain computer interface , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  A. Engel,et al.  An independent brain–computer interface using covert non-spatial visual selective attention , 2010, Journal of neural engineering.

[13]  G. Pfurtscheller,et al.  Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces? , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  José del R. Millán,et al.  Adaptive brain interfaces , 2003, Commun. ACM.

[15]  C. Mehring,et al.  Inference of hand movements from local field potentials in monkey motor cortex , 2003, Nature Neuroscience.

[16]  Thomas Hofmann,et al.  A brain computer interface with online feedback based on magnetoencephalography , 2005, ICML.

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

[18]  Ronald M. Aarts,et al.  A Survey of Stimulation Methods Used in SSVEP-Based BCIs , 2010, Comput. Intell. Neurosci..

[19]  K.-R. Muller,et al.  Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.

[20]  F Babiloni,et al.  Linear classification of low-resolution EEG patterns produced by imagined hand movements. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[21]  D. McFarland,et al.  An auditory brain–computer interface (BCI) , 2008, Journal of Neuroscience Methods.

[22]  Matthew H. Davis,et al.  Detecting awareness in the vegetative state. , 2006, Science.

[23]  M. Sams,et al.  Classification of single MEG trials related to left and right index finger movements , 2006, Clinical Neurophysiology.

[24]  Klaus-Robert Müller,et al.  The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects , 2008, IEEE Transactions on Biomedical Engineering.

[25]  N. Birbaumer,et al.  An auditory oddball brain–computer interface for binary choices , 2010, Clinical Neurophysiology.

[26]  H. Strasburger.  Unfocused spatial attention underlies the crowding effect in indirect form vision. , 2004, Journal of vision.

[27]  A. Kübler,et al.  A Brain–Computer Interface Controlled Auditory Event‐Related Potential (P300) Spelling System for Locked‐In Patients , 2009, Annals of the New York Academy of Sciences.

[28]  Cuntai Guan,et al.  Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain–computer interface , 2007, NeuroImage.

[29]  Klaus-Robert Müller,et al.  The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.

[30]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[31]  J. Wolpaw,et al.  Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface , 2005, Neurology.

[32]  Benjamin Blankertz,et al.  Designing for uncertain, asymmetric control: Interaction design for brain-computer interfaces , 2009, Int. J. Hum. Comput. Stud..

[33]  Ethan R. Buch,et al.  Think to Move: a Neuromagnetic Brain-Computer Interface (BCI) System for Chronic Stroke , 2008, Stroke.

[34]  D J McFarland,et al.  Brain-computer interface research at the Wadsworth Center. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[35]  Gert Pfurtscheller,et al.  Development, set-up and first results for a one-channel near-infrared spectroscopy system / Entwicklung, Aufbau und vorläufige Ergebnisse eines Einkanal- Nahinfrarot-Spektroskopie-Systems , 2008, Biomedizinische Technik. Biomedical engineering.

[36]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[37]  N. Birbaumer,et al.  An auditory oddball (P300) spelling system for brain-computer interfaces. , 2009, Psychophysiology.

[38]  Bijan Pesaran,et al.  Temporal structure in neuronal activity during working memory in macaque parietal cortex , 2000, Nature Neuroscience.

[39]  J. Wolpaw,et al.  Brain-computer communication: unlocking the locked in. , 2001, Psychological bulletin.

[40]  K.-R. Muller,et al.  Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[41]  P R Kennedy,et al.  Direct control of a computer from the human central nervous system. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[42]  Benjamin Blankertz,et al.  Towards a Cure for BCI Illiteracy , 2009, Brain Topography.

[43]  Ricardo Chavarriaga,et al.  Visuo-Spatial Attention Frame Recognition for Brain-Computer Interfaces , 2007 .

[44]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[45]  N. Birbaumer,et al.  The thought translation device: a neurophysiological approach to communication in total motor paralysis , 1999, Experimental Brain Research.

[46]  José del R. Millán,et al.  Brain-actuated interaction , 2004, Artif. Intell..

[47]  José del R. Millán,et al.  Non-invasive estimation of local field potentials for neuroprosthesis control , 2005, Cognitive Processing.

[48]  Thierry Baccino,et al.  Eye-Fixation-Related Potentials: Insight into Parafoveal Processing , 2005 .

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

[50]  N. Birbaumer,et al.  Brain-computer communication: self-regulation of slow cortical potentials for verbal communication. , 2001, Archives of physical medicine and rehabilitation.

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

[52]  José del R. Millán,et al.  You Are Wrong! - Automatic Detection of Interaction Errors from Brain Waves , 2005, IJCAI.

[53]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[54]  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.

[55]  Benjamin Blankertz,et al.  Two-dimensional auditory p300 speller with predictive text system , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[56]  Christoph M. Michel,et al.  Electrical neuroimaging based on biophysical constraints , 2004, NeuroImage.

[57]  John Paulin Hansen,et al.  New technological windows into mind: there is more in eyes and brains for human-computer interaction , 1996, CHI.

[58]  Benjamin Blankertz,et al.  A novel brain-computer interface based on the rapid serial visual presentation paradigm , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[59]  J. Wolpaw,et al.  Towards an independent brain–computer interface using steady state visual evoked potentials , 2008, Clinical Neurophysiology.