P300 brain computer interface: current challenges and emerging trends

A brain-computer interface (BCI) enables communication without movement based on brain signals measured with electroencephalography (EEG). BCIs usually rely on one of three types of signals: the P300 and other components of the event-related potential (ERP), steady state visual evoked potential (SSVEP), or event related desynchronization (ERD). Although P300 BCIs were introduced over twenty years ago, the past few years have seen a strong increase in P300 BCI research. This closed-loop BCI approach relies on the P300 and other components of the ERP, based on an oddball paradigm presented to the subject. In this paper, we overview the current status of P300 BCI technology, and then discuss new directions: paradigms for eliciting P300s; signal processing methods; applications; and hybrid BCIs. We conclude that P300 BCIs are quite promising, as several emerging directions have not yet been fully explored and could lead to improvements in bit rate, reliability, usability, and flexibility.

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

[2]  E. Fetz,et al.  The neurochip BCI: towards a neural prosthesis for upper limb function , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[4]  Günter Edlinger,et al.  Social Environments, Mixed Communication and Goal-Oriented Control Application Using a Brain-Computer Interface , 2011, HCI.

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

[6]  Eric W. Sellers,et al.  Predictive Spelling With a P300-Based Brain–Computer Interface: Increasing the Rate of Communication , 2010, Int. J. Hum. Comput. Interact..

[7]  Fusheng Yang,et al.  BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications , 2004, IEEE Transactions on Biomedical Engineering.

[8]  J D Bayliss,et al.  A virtual reality testbed for brain-computer interface research. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[9]  G Pfurtscheller,et al.  Toward a hybrid brain–computer interface based on imagined movement and visual attention , 2010, Journal of neural engineering.

[10]  G. Edlinger,et al.  State-of-the-Art in BCI Research: BCI Award 2010 , 2011 .

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

[12]  M. Eimer.  Event-related brain potentials distinguish processing stages involved in face perception and recognition , 2000, Clinical Neurophysiology.

[13]  Eric W Sellers,et al.  Manipulating attention via mindfulness induction improves P300-based brain–computer interface performance , 2011, Journal of neural engineering.

[14]  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.

[15]  J. Wolpaw,et al.  Does the ‘P300’ speller depend on eye gaze? , 2010, Journal of neural engineering.

[16]  R. Fazel-Rezai,et al.  Human Error in P300 Speller Paradigm for Brain-Computer Interface , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Ying Sun,et al.  Asynchronous P300 BCI: SSVEP-based control state detection , 2010, 2010 18th European Signal Processing Conference.

[18]  A F Sanders,et al.  The Eriksen flanker effect revisited. , 2002, Acta psychologica.

[19]  Tao Liu,et al.  N200-speller using motion-onset visual response , 2009, Clinical Neurophysiology.

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

[21]  Reza Fazel-Rezai,et al.  A Comparison among Several P300 Brain-Computer Interface Speller Paradigms , 2011, Clinical EEG and neuroscience.

[22]  Iñaki Iturrate,et al.  A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation , 2009, IEEE Transactions on Robotics.

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

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

[25]  Brendan Z. Allison.  The I of BCIs: Next Generation Interfaces for Brain-Computer Interface Systems That Adapt to Individual Users , 2009, HCI.

[26]  G R Müller-Putz,et al.  Toward smarter BCIs: extending BCIs through hybridization and intelligent control , 2012, Journal of neural engineering.

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

[28]  Mel Slater,et al.  Goal-Oriented Control with Brain-Computer Interface , 2009, HCI.

[29]  Clare D. McGillem,et al.  Measurements of Signal Components in Single Visualy Evoked Brain Potentials , 1977, IEEE Transactions on Biomedical Engineering.

[30]  R. Fazel-Rezai,et al.  Analysis of P300 Classifiers in Brain Computer Interface Speller , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[31]  B.Z. Allison,et al.  ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[32]  D.J. McFarland,et al.  The wadsworth BCI research and development program: at home with BCI , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  J. Polich,et al.  P300 amplitude is determined by target-to-target interval. , 2002, Psychophysiology.

[34]  Günter Edlinger,et al.  A Hybrid Brain-Computer Interface for Smart Home Control , 2011, HCI.

[35]  Anne-Marie Brouwer,et al.  A tactile P 300 brain-computer interface , 2010 .

[36]  R. Heuser.  Surprise, surprise , 2014, Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions.

[37]  Jonathan R Wolpaw,et al.  A brain-computer interface for long-term independent home use , 2010, Amyotrophic lateral sclerosis : official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases.

[38]  Rajesh P. N. Rao,et al.  Control of a humanoid robot by a noninvasive brain–computer interface in humans , 2008, Journal of neural engineering.

[39]  H. Flor,et al.  The thought translation device (TTD) for completely paralyzed patients. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[40]  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.

[41]  Donatella Mattia,et al.  A Brain-Computer Interface as Input Channel for a Standard Assistive Technology Software , 2011, Clinical EEG and neuroscience.

[42]  N. Kanwisher.  Repetition blindness: Type recognition without token individuation , 1987, Cognition.

[43]  Yuanqing Li,et al.  An EEG-Based BCI System for 2-D Cursor Control by Combining Mu/Beta Rhythm and P300 Potential , 2010, IEEE Transactions on Biomedical Engineering.

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

[45]  P Radtke.  [Art and rehabilitation]. , 1994, Die Rehabilitation.

[46]  Xiaorong Gao,et al.  A brain–computer interface using motion-onset visual evoked potential , 2008, Journal of neural engineering.

[47]  M. Thulasidas,et al.  Robust classification of EEG signal for brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[48]  A Kübler,et al.  A P 300-based brain-computer interface for people with amyotrophic lateral sclerosis , 2010 .

[49]  C. Cinel,et al.  P300-Based BCI Mouse With Genetically-Optimized Analogue Control , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[50]  Xingyu Wang,et al.  A new P300 stimulus presentation pattern for EEG-based spelling systems , 2010, Biomedizinische Technik. Biomedical engineering.

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

[52]  R. Fazel-Rezai,et al.  Assessment of Preprocessing on Classifiers Used in the P300 Speller Paradigm , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[53]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[54]  J. Wolpaw,et al.  A P300-based brain–computer interface for people with amyotrophic lateral sclerosis , 2008, Clinical Neurophysiology.

[55]  Febo Cincotti,et al.  Out of the frying pan into the fire--the P300-based BCI faces real-world challenges. , 2011, Progress in brain research.

[56]  C. Herrmann.  Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena , 2001, Experimental Brain Research.

[57]  Jan B. F. van Erp,et al.  A Tactile P300 Brain-Computer Interface , 2010, Front. Neurosci..

[58]  J. Wolpaw,et al.  A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns , 2010, Clinical Neurophysiology.

[59]  A. Kübler,et al.  Motivation modulates the P300 amplitude during brain–computer interface use , 2010, Clinical Neurophysiology.

[60]  Brendan Z. Allison,et al.  Workload assessment of computer gaming using a single-stimulus event-related potential paradigm , 2008, Biological Psychology.

[61]  Reza Fazel-Rezai,et al.  A region-based P300 speller for brain-computer interface , 2009, Canadian Journal of Electrical and Computer Engineering.

[62]  E R John,et al.  Information Delivery and the Sensory Evoked Potential , 1967, Science.

[63]  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.

[64]  M Salvaris,et al.  Visual modifications on the P300 speller BCI paradigm , 2009, Journal of neural engineering.

[65]  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.

[66]  Yi Li,et al.  A hybrid brain-computer interface control strategy in a virtual environment , 2011, Journal of Zhejiang University SCIENCE C.

[67]  G.F. Inbar,et al.  An improved P300-based brain-computer interface , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[68]  E. Donchin,et al.  Is the P300 component a manifestation of context updating? , 1988, Behavioral and Brain Sciences.

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

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

[71]  S M M Martens,et al.  Overlap and refractory effects in a brain–computer interface speller based on the visual P300 event-related potential , 2009, Journal of neural engineering.

[72]  J. Wolpaw,et al.  A P300 event-related potential brain–computer interface (BCI): The effects of matrix size and inter stimulus interval on performance , 2006, Biological Psychology.

[73]  Shangkai Gao,et al.  An online brain–computer interface using non-flashing visual evoked potentials , 2010, Journal of neural engineering.

[74]  C. Neuper,et al.  Toward a high-throughput auditory P300-based brain–computer interface , 2009, Clinical Neurophysiology.

[75]  Bohn Stafleu van Loghum,et al.  Online … , 2002, LOG IN.

[76]  F. Piccione,et al.  P300-based brain computer interface: Reliability and performance in healthy and paralysed participants , 2006, Clinical Neurophysiology.