Introducing the Edges Paradigm: A P300 Brain–Computer Interface for Spelling Written Words

P300-based brain-computer interface spellers employ the P300 component, which is derived from scalp measured electroencephalogram during the brain's electrical response to a flash denoting an attended target character. The most popular P300 speller, the row-column paradigm (RCP), displays characters in a matrix within which rows and columns of characters are flashed eliciting P300 responses when the illuminated row or column contains the attended target character. Despite being a longstanding successful approach, this RCP faces several challenges, including the adjacency and crowding problems. A new P300 speller is introduced-the edges paradigm (EP). Distinct from existing P300 spellers, the EP presents a square adjacent to each column or row in the outer boundary of the matrix. By replacing each flash of a row or column with that square, this EP exhibited attenuated influences of crowding and adjacency-problems known to perturb the RCP. In the copy-spelling mode, 14 neurologically normal participants demonstrated an improved accuracy of 93.3 ± 2.0% for the EP relative to 81.7 ± 2.8% for the RCP, alongside a faster communication rate. Subjective ratings also indicated that the EP caused significantly less fatigue, while increasing alertness and comfort.

[1]  J. R. Wolpaw,et al.  ' s personal copy A novel P 300-based brain – computer interface stimulus presentation paradigm : Moving beyond rows and columns q , 2010 .

[2]  Maarten De Vos,et al.  P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier , 2014, Journal of neural engineering.

[3]  R. Logie,et al.  Age-of-acquisition, imagery, concreteness, familiarity, and ambiguity measures for 1,944 words , 1980 .

[4]  Tomislav Milekovic,et al.  Low-latency multi-threaded processing of neuronal signals for brain-computer interfaces , 2014, Front. Neuroeng..

[5]  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.

[6]  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.

[7]  Doru Talaba,et al.  P300-Based Brain-Neuronal Computer Interaction for Spelling Applications , 2013, IEEE Transactions on Biomedical Engineering.

[8]  N. Crone,et al.  High-frequency gamma oscillations and human brain mapping with electrocorticography. , 2006, Progress in brain research.

[9]  M S Treder,et al.  Gaze-independent brain–computer interfaces based on covert attention and feature attention , 2011, Journal of neural engineering.

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

[11]  Jun Kong,et al.  The zigzag paradigm: a new P300-based brain computer interface , 2013, ICMI '13.

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

[13]  N. Birbaumer,et al.  Prediction of Auditory and Visual P300 Brain-Computer Interface Aptitude , 2013, PloS one.

[14]  Yili Liu,et al.  EEG-Based Brain-Controlled Mobile Robots: A Survey , 2013, IEEE Transactions on Human-Machine Systems.

[15]  D. Levi,et al.  Visual crowding: a fundamental limit on conscious perception and object recognition , 2011, Trends in Cognitive Sciences.

[16]  Sheng He,et al.  Horizontal and vertical asymmetry in visual spatial crowding effects. , 2007, Journal of vision.

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

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

[19]  Boris M. Velichkovsky,et al.  Eye typing in application: A comparison of two systems with ALS patients , 2008 .

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

[21]  Gerwin Schalk,et al.  Rapid Communication with a “P300” Matrix Speller Using Electrocorticographic Signals (ECoG) , 2010, Front. Neurosci..

[22]  Z. Simmons,et al.  Quality of life in ALS depends on factors other than strength and physical function , 2000, Neurology.

[23]  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.

[24]  E. John,et al.  Evoked-Potential Correlates of Stimulus Uncertainty , 1965, Science.

[25]  Bruce J. Fisch,et al.  Fisch and Spehlmann's Eeg Primer: Basic Principles of Digital and Analog Eeg , 1999 .

[26]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

[28]  S. Luck.  Ten Simple Rules for Designing and Interpreting ERP Experiments , 2003 .

[29]  Roderick Murray-Smith,et al.  Optimized stimulation events for a visual ERP BCI , 2011 .

[30]  Tobias Kaufmann,et al.  Beyond maximum speed—a novel two-stimulus paradigm for brain–computer interfaces based on event-related potentials (P300-BCI) , 2014, Journal of neural engineering.

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

[32]  E W Sellers,et al.  Suppressing flashes of items surrounding targets during calibration of a P300-based brain–computer interface improves performance , 2011, Journal of neural engineering.

[33]  Andrzej Cichocki,et al.  EEG Correlates of Voice and Face Emotional Judgments in the Human Brain , 2015, Cognitive Computation.

[34]  M. Morgan,et al.  The Role of Target Salience in Crowding , 2005, Perception.

[35]  Haihong Zhang,et al.  A Brain-Controlled Wheelchair Based on P300 and Path Guidance , 2006, The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006. BioRob 2006..

[36]  Benjamin Blankertz,et al.  A Note on Brain Actuated Spelling with the Berlin Brain-Computer Interface , 2007, HCI.

[37]  Yoshikazu Nakajima,et al.  A Two-Level Predictive Event-Related Potential-Based Brain–Computer Interface , 2013, IEEE Transactions on Biomedical Engineering.

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

[39]  Max Coltheart,et al.  The MRC Psycholinguistic Database , 1981 .

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

[41]  S. Baumann,et al.  Localization of the P3 sources using magnetoencephalography and magnetic resonance imaging. , 1991, Electroencephalography and clinical neurophysiology.

[42]  J. Palva,et al.  Very Slow EEG Fluctuations Predict the Dynamics of Stimulus Detection and Oscillation Amplitudes in Humans , 2008, The Journal of Neuroscience.

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

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

[45]  Marc M. Van Hulle,et al.  Comparison of Classification Methods for P300 Brain-Computer Interface on Disabled Subjects , 2011, Comput. Intell. Neurosci..

[46]  Konstantinos Priftis,et al.  Effectiveness of the P3-speller in brain–computer interfaces for amyotrophic lateral sclerosis patients: a systematic review and meta-analysis , 2014, Front. Neuroeng..

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

[48]  Ping Yang,et al.  An Empirical Bayesian Framework for Brain–Computer Interfaces , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[49]  H. BOUMA,et al.  Interaction Effects in Parafoveal Letter Recognition , 1970, Nature.

[50]  F. Babiloni,et al.  A covert attention P300-based brain–computer interface: Geospell , 2012, Ergonomics.

[51]  J. Polich.  Updating P 300 : An Integrative Theory of P 3 a and P 3 b , 2009 .

[52]  Emanuel Donchin,et al.  The Component Structure of Event-Related Potentials in the P300 Speller Paradigm , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[53]  Michael Tangermann,et al.  How stimulation speed affects Event-Related Potentials and BCI performance , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[54]  M. Scherg,et al.  Localizing P300 Generators in Visual Target and Distractor Processing: A Combined Event-Related Potential and Functional Magnetic Resonance Imaging Study , 2004, The Journal of Neuroscience.

[55]  F Babiloni,et al.  P300-based brain–computer interface for environmental control: an asynchronous approach , 2011, Journal of neural engineering.

[56]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[57]  Shaohan Hu,et al.  NeuroPhone: brain-mobile phone interface using a wireless EEG headset , 2010, MobiHeld '10.

[58]  J. Wolpaw,et al.  Toward enhanced P 300 speller performance , 2007 .

[59]  Tobias Kaufmann,et al.  Comparison of tactile, auditory, and visual modality for brain-computer interface use: a case study with a patient in the locked-in state , 2013, Front. Neurosci..

[60]  Urbano Nunes,et al.  GIBS block speller: Toward a gaze-independent P300-based BCI , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[61]  S. Debener,et al.  How about taking a low-cost, small, and wireless EEG for a walk? , 2012, Psychophysiology.

[62]  Benjamin Blankertz,et al.  Data driven neuroergonomic optimization of BCI stimuli , 2011 .

[63]  Touradj Ebrahimi,et al.  An efficient P300-based brain–computer interface for disabled subjects , 2008, Journal of Neuroscience Methods.

[64]  S. Luck,et al.  The effects of electrode impedance on data quality and statistical significance in ERP recordings. , 2010, Psychophysiology.

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

[66]  Jason Farquhar,et al.  Interactions Between Pre-Processing and Classification Methods for Event-Related-Potential Classification , 2012, Neuroinformatics.

[67]  H. Lüders,et al.  American Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature , 1991, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[68]  Eric W. Sellers,et al.  A general P300 brain–computer interface presentation paradigm based on performance guided constraints , 2012, Neuroscience Letters.

[69]  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.

[70]  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.

[71]  W. Singer,et al.  Gamma or no gamma, that is the question , 2014, Trends in Cognitive Sciences.

[72]  B. Velichkovsky,et al.  Eye typing in application: A comparison of two interfacing systems with ALS patients , 2008 .

[73]  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.

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

[75]  Fanglin Chen,et al.  A Speedy Hybrid BCI Spelling Approach Combining P300 and SSVEP , 2014, IEEE Transactions on Biomedical Engineering.

[76]  Michael Tangermann,et al.  Natural stimuli improve auditory BCIs with respect to ergonomics and performance. , 2012, Journal of neural engineering.

[77]  A. Schlögl,et al.  Artifact Processing in Computerized Analysis of Sleep EEG – A Review , 1999, Neuropsychobiology.

[78]  J. Polich.  Theoretical Overview of P3a and P3b , 2003 .