How Many People Can Use a BCI System

Most brain–computer interface (BCI) systems utilize one of three approaches: sensorimotor rhythms (SMRs), P300s, or steady-state visually evoked potentials (SSVEPs). Numerous groups have reported that these approaches do not provide effective communication for a small percentage of users. This phenomenon has been called BCI illiteracy, inefficiency, or other terms. This chapter reviews this challenge across the three major BCI approaches. We review studies with a large number of users to assess how many people can use each type of BCI and discuss new efforts that could bring BCIs to broader user groups. Improved signal processing and feedback could benefit SMR BCI users, the face-speller may help P300 BCI users, and limited training could help SSVEP BCI users. Nonvisual BCIs could also enable people who are minimally conscious to answer “yes” or “no” questions. While there remain some people who cannot use a BCI, progress is being made to extend BCI technology to broader groups.

[1]  Brendan Z Allison,et al.  Effects of SOA and flash pattern manipulations on ERPs, performance, and preference: implications for a BCI system. , 2006, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[2]  Brendan Z. Allison,et al.  How Many People Could Use an SSVEP BCI? , 2012, Front. Neurosci..

[3]  V. Caggiano,et al.  Proprioceptive Feedback and Brain Computer Interface (BCI) Based Neuroprostheses , 2012, PloS one.

[4]  José del R. Millán,et al.  An Introduction to Brain-Computer Interfacing , 2007 .

[5]  Brendan Z. Allison,et al.  Comparison of Dry and Gel Based Electrodes for P300 Brain–Computer Interfaces , 2012, Front. Neurosci..

[6]  T. Jung,et al.  Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  Horst Bischof,et al.  A practical procedure for real-time functional mapping of eloquent cortex using electrocorticographic signals in humans , 2009, Epilepsy & Behavior.

[8]  Brendan Z. Allison,et al.  Brain-Computer Interfaces , 2010 .

[9]  J. Peters,et al.  Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery , 2011, Journal of neural engineering.

[10]  A. Kübler,et al.  Face stimuli effectively prevent brain–computer interface inefficiency in patients with neurodegenerative disease , 2013, Clinical Neurophysiology.

[11]  E. Sellers,et al.  How many people are able to control a P300-based brain–computer interface (BCI)? , 2009, Neuroscience Letters.

[12]  Brendan Z. Allison,et al.  Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction , 2013 .

[13]  J. Wolpaw,et al.  Brain-Computer Interfaces: Principles and Practice , 2012 .

[14]  Srivas Chennu,et al.  Bedside detection of awareness in the vegetative state: a cohort study , 2011, The Lancet.

[15]  Brendan Z. Allison,et al.  Could Anyone Use a BCI? , 2010, Brain-Computer Interfaces.

[16]  Rupert Ortner,et al.  A Motor Imagery Based Brain-Computer Interface for Stroke Rehabilitation , 2012, Annual Review of Cybertherapy and Telemedicine.

[17]  Xiaorong Gao,et al.  An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method , 2009, Journal of neural engineering.

[18]  K. Kansaku,et al.  Operation of a P300-based brain–computer interface by individuals with cervical spinal cord injury , 2011, Clinical Neurophysiology.

[19]  M. Monti,et al.  Cognition in the vegetative state. , 2012, Annual review of clinical psychology.

[20]  Rupert Ortner,et al.  Human-Computer Confluence for Rehabilitation Purposes after Stroke , 2013, HCI.

[21]  E. Donchin,et al.  Brain-computer interface research at the university of south Florida cognitive psychophysiology laboratory: the P300 speller , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  G. Pfurtscheller,et al.  How many people are able to operate an EEG-based brain-computer interface (BCI)? , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  Jyh-Yeong Chang,et al.  Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors , 2012, Journal of NeuroEngineering and Rehabilitation.

[24]  Xingyu Wang,et al.  P300 Chinese input system based on Bayesian LDA , 2010, Biomedizinische Technik. Biomedical engineering.

[25]  A. Cichocki,et al.  The Changing Face of P300 BCIs: A Comparison of Stimulus Changes in a P300 BCI Involving Faces, Emotion, and Movement , 2012, PloS one.

[26]  B. Allison,et al.  BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI? , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[27]  Jonathan R. Wolpaw,et al.  Brain–Computer InterfacesPrinciples and Practice , 2012 .

[28]  Clemens Brunner,et al.  BCI Software Platforms , 2012 .

[29]  Anatole Lécuyer,et al.  Combining BCI with Virtual Reality: Towards New Applications and Improved BCI , 2012 .

[30]  Peng Yuan,et al.  A study of the existing problems of estimating the information transfer rate in online brain–computer interfaces , 2013, Journal of neural engineering.

[31]  K. Jellinger.  Toward Brain-Computer Interfacing , 2009 .

[32]  Brendan Z. Allison,et al.  Trends in BCI research: progress today, backlash tomorrow? , 2011, XRDS.

[33]  Brendan Z. Allison,et al.  P300 brain computer interface: current challenges and emerging trends , 2012, Front. Neuroeng..

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

[35]  G. Pfurtscheller,et al.  Designing optimal spatial filters for single-trial EEG classification in a movement task , 1999, Clinical Neurophysiology.

[36]  G. Pfurtscheller,et al.  An SSVEP BCI to Control a Hand Orthosis for Persons With Tetraplegia , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[38]  Karla Felix Navarro,et al.  A Comprehensive Survey of Brain Interface Technology Designs , 2007, Annals of Biomedical Engineering.

[39]  Anton Nijholt,et al.  Towards Practical Brain-Computer Interfaces: Bridging the Gap from Research to Real-World Applications , 2012 .

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

[41]  Brendan Z. Allison,et al.  Is It Significant? Guidelines for Reporting BCI Performance , 2012 .

[42]  N Birbaumer,et al.  A binary spelling interface with random errors. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[43]  Yijun Wang,et al.  A high-speed BCI based on code modulation VEP , 2011, Journal of neural engineering.

[44]  Ricardo Chavarriaga,et al.  A hybrid brain–computer interface based on the fusion of electroencephalographic and electromyographic activities , 2011, Journal of neural engineering.

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

[46]  Antinus Nijholt,et al.  A Preliminary Survey on the Perception of Marketability of Brain-Computer Interfaces and Initial Development of a Repository of BCI Companies , 2011 .

[47]  Rupert Ortner,et al.  How many people can control a motor imagery based BCI using common spatial patterns? , 2015, 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER).

[48]  R. Goebel,et al.  Brain–computer interfaces for communication with nonresponsive patients , 2012, Annals of neurology.

[49]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[50]  Gang Qian,et al.  Systems and Applications , 2009 .

[51]  Brendan Z. Allison,et al.  The Hybrid BCI , 2010, Frontiers in Neuroscience.

[52]  A. Kübler,et al.  Brain Painting: First Evaluation of a New Brain–Computer Interface Application with ALS-Patients and Healthy Volunteers , 2010, Front. Neurosci..

[53]  Brendan Z. Allison,et al.  Brain–computer interfacing: more than the sum of its parts , 2012, Soft Computing.

[54]  Brendan Z. Allison,et al.  Toward Ubiquitous BCIs , 2009 .

[55]  Cuntai Guan,et al.  A Large Clinical Study on the Ability of Stroke Patients to Use an EEG-Based Motor Imagery Brain-Computer Interface , 2011, Clinical EEG and neuroscience.

[56]  W. Martin Usrey,et al.  Patterned Activity within the Local Cortical Architecture , 2010, Front. Neurosci..

[57]  Brendan Z. Allison,et al.  Poor performance in SSVEP BCIs: Are worse subjects just slower? , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[58]  G. Pfurtscheller,et al.  Rapid prototyping of an EEG-based brain-computer interface (BCI) , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[59]  Christoph Guger,et al.  Augmented control of an avatar using an SSVEP based BCI , 2012, AH '12.

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

[61]  Brendan Z. Allison,et al.  Brain-Computer Interface Research , 2019, SpringerBriefs in Electrical and Computer Engineering.

[62]  J. Mourino,et al.  Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.