Application of P300 Event-Related Potential in Brain-Computer Interface

The primary purpose of this chapter is to demonstrate one of the applications of P300 event-related potential (ERP), i.e., brain-computer interface (BCI). Researchers and students will find the chapter appealing with a preliminary description of P300 ERP. This chapter also appreciates the importance and advantages of noninvasive ERP technique. In noninvasive BCI, the P300 ERPs are extracted from brain electrical activities [electroencephalogram (EEG)] as a signature of the underlying electrophysiological mechanism of brain responses to the external or internal changes and events. As the chapter proceeds, topics are covered on more relevant scholarly works about challenges and new directions in P300 BCI. Along with these, articles with the references on the advancement of this technique will be presented to ensure that the scholarly reviews are accessible to people who are new to this field. To enhance fundamental understanding, stimulation as well as signal processing methods will be discussed from some novel works with a comparison of the associated results. This chapter will meet the need for a concise and practical description of basic, as well as advanced P300 ERP techniques, which is suitable for a broad range of researchers extending from today’s novice to an experienced cognitive researcher.

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

[2]  Tzyy-Ping Jung,et al.  A Collaborative Brain-Computer Interface for Accelerating Human Decision Making , 2013, HCI.

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

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

[5]  M. Häusser,et al.  Electrophysiology in the age of light , 2009, Nature.

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

[7]  M. Riadh Arefin,et al.  A review of methods and applications of brain computer interface systems , 2016, 2016 IEEE International Conference on Electro Information Technology (EIT).

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

[9]  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.

[10]  D J McFarland,et al.  An EEG-based brain-computer interface for cursor control. , 1991, Electroencephalography and clinical neurophysiology.

[11]  Alain Rakotomamonjy,et al.  BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller , 2008, IEEE Transactions on Biomedical Engineering.

[12]  Reza Fazel-Rezai,et al.  A Review of Hybrid Brain-Computer Interface Systems , 2013, Adv. Hum. Comput. Interact..

[13]  E. Donchin,et al.  A P300-based brain–computer interface: Initial tests by ALS patients , 2006, Clinical Neurophysiology.

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

[15]  K B Campbell,et al.  Event-related potentials and recognition memory for pictures and words: the effects of intentional and incidental learning. , 1990, Psychophysiology.

[16]  Touradj Ebrahimi,et al.  Bayesian feature selection applied in a p300 brain-computer interface , 2008, 2008 16th European Signal Processing Conference.

[17]  R. Johnson.  A triarchic model of P300 amplitude. , 1986, Psychophysiology.

[18]  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.

[19]  L. Farwell,et al.  Using brain MERMER testing to detect knowledge despite efforts to conceal. , 2001, Journal of forensic sciences.

[20]  Wen-Wen Chang,et al.  Functional brain network and multichannel analysis for the P300-based brain computer interface system of lying detection , 2016, Expert Syst. Appl..

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

[22]  Dirk Heylen,et al.  Bacteria Hunt: A multimodal, multiparadigm BCI game , 2010 .

[23]  Dylan D. Schmorrow,et al.  Augmented Cognition: An Overview , 2009 .

[24]  Reza Fazel-Rezai,et al.  Brain Signals: Feature Extraction and Classification Using Rough Set Methods , 2005, RSFDGrC.

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

[26]  J. Polich,et al.  P3a and P3b from typical auditory and visual stimuli , 1999, Clinical Neurophysiology.

[27]  Miguel Castelo-Branco,et al.  Visual P300-based BCI to steer a wheelchair: A Bayesian approach , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[28]  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.

[29]  Reza Fazel-Rezai,et al.  Reducing human error in P300 speller paradigm for brain-computer interface , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[30]  K. Tavakolian,et al.  A New Region-based BCI Speller Design using Steady State Visual Evoked Potentials , 2016 .

[31]  Reza Fazel-Rezai,et al.  Recent Advances in Brain-Computer Interface Systems , 2011 .

[32]  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.

[33]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

[35]  Reza Fazel-Rezai,et al.  A comparison between a matrix-based and a region-based P300 speller paradigms for brain-computer interface , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[37]  Helge J. Ritter,et al.  BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm , 2004, IEEE Transactions on Biomedical Engineering.

[38]  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.

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

[40]  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.

[41]  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.

[42]  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.

[43]  K L Shapiro,et al.  Temporary suppression of visual processing in an RSVP task: an attentional blink? . , 1992, Journal of experimental psychology. Human perception and performance.

[44]  E. Donchin,et al.  P300 and recall in an incidental memory paradigm. , 1986, Psychophysiology.

[45]  H. Shibasaki.  Human brain mapping: Hemodynamic response and electrophysiology , 2008, Clinical Neurophysiology.

[46]  R. Knight.  Contribution of human hippocampal region to novelty detection , 1996, Nature.

[47]  Christian Jutten,et al.  " Brain Invaders": a prototype of an open-source P300-based video game working with the OpenViBE platform , 2011 .

[48]  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.

[49]  Wolfgang Rosenstiel,et al.  Control of an Internet Browser Using the P300 Event- Related Potential , 2008 .

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

[51]  M. Pauls,et al.  A Low-Cost Biomedical Signal Transceiver based on a Bluetooth Wireless System , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[52]  Emanuel Donchin,et al.  The P300 component of the event-related brain potential as an index of information processing , 1982, Biological Psychology.

[53]  Weidong Zhou,et al.  BLDA Approach for Classifying P300 Potential , 2008 .

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

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

[56]  J. Polich,et al.  Task difficulty, probability, and inter-stimulus interval as determinants of P300 from auditory stimuli. , 1987, Electroencephalography and clinical neurophysiology.

[57]  E Donchin,et al.  The truth will out: interrogative polygraphy ("lie detection") with event-related brain potentials. , 1991, Psychophysiology.

[58]  Luca T. Mainardi,et al.  Online Detection of P300 and Error Potentials in a BCI Speller , 2010, Comput. Intell. Neurosci..

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

[60]  C.W. Anderson,et al.  Comparison of linear, nonlinear, and feature selection methods for EEG signal classification , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[61]  Helge J. Ritter,et al.  2009 Special Issue: The MindGame: A P300-based brain-computer interface game , 2009 .

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

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

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

[65]  E. Donchin.  Presidential address, 1980. Surprise!...Surprise? , 1981, Psychophysiology.

[66]  Reza Fazel-Rezai,et al.  Mental workload and task engagement evaluation based on changes in electroencephalogram , 2012 .

[67]  Stephen J. Morrissey,et al.  Reviews of Human Factors and Ergonomics , 2008 .

[68]  Sonja C. Kleih,et al.  Toward a P300 Based Brain-Computer Interface for Aphasia Rehabilitation after Stroke: Presentation of Theoretical Considerations and a Pilot Feasibility Study , 2016, Front. Hum. Neurosci..

[69]  Roderick Murray-Smith,et al.  Using Simulated Input into Brain-Computer Interfaces for User-Centred Design , 2011 .

[70]  E Donchin,et al.  Effects of mnemonic strategy manipulation in a Von Restorff paradigm. , 1990, Electroencephalography and clinical neurophysiology.