An ERP-based BCI with peripheral stimuli: validation with ALS patients

Many studies reported that ERP-based BCIs can provide communication for some people with amyotrophic lateral sclerosis (ALS). ERP-based BCIs often present characters within a matrix that occupies the center of the visual field. However, several studies have identified some concerns with the matrix-based approach. This approach may lead to fatigue and errors resulting from flashing adjacent stimuli, and is impractical for users who might want to use the BCI in tandem with other software or feedback in the center of the monitor. In this paper, we introduce and validate an alternate ERP-based BCI display approach. By presenting stimuli near the periphery of the display, we reduce the adjacency problem and leave the center of the display available for feedback or other applications. Two ERP-based display approaches were tested on 18 ALS patients to: (1) compare performance between a conventional matrix speller paradigm (Matrix-P, mean visual angle 6°) and a new speller paradigm with peripherally distributed stimuli (Peripheral-P, mean visual angle 8.8°); and (2) assess performance while spelling 42 characters online continuously, without a break. In the Peripheral-P condition, 12 subjects attained higher than 80% feedback accuracy during online performance, and 7 of these subjects obtained higher than 90% accuracy. The experimental results showed that the Peripheral-P condition yielded performance comparable to the conventional Matrix-P condition ( p  > 0.05) in accuracy and information transfer rate. This paper introduces a new display approach that leaves the center of the monitor open for feedback and/or other display elements, such as movies, games, art, or displays from other AAC software or conventional software tools.

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

[2]  Xin Zhao,et al.  Use of a steady-state baseline to address evoked vs. oscillation models of visual evoked potential origin , 2016, NeuroImage.

[3]  Dewen Hu,et al.  Usage of drip drops as stimuli in an auditory P300 BCI paradigm , 2018, Cognitive Neurodynamics.

[4]  J. Farquhar,et al.  Comparing tactile and visual gaze-independent brain–computer interfaces in patients with amyotrophic lateral sclerosis and healthy users , 2014, Clinical Neurophysiology.

[5]  A. Al-Chalabi,et al.  A proposed staging system for amyotrophic lateral sclerosis , 2012, Brain : a journal of neurology.

[6]  Yang Yu,et al.  A Dynamically Optimized SSVEP Brain–Computer Interface (BCI) Speller , 2015, IEEE Transactions on Biomedical Engineering.

[7]  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.

[8]  Chun-Yen Chang,et al.  Evaluate the Feasibility of Using Frontal SSVEP to Implement an SSVEP-Based BCI in Young, Elderly and ALS Groups , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[10]  Yuanqing Li,et al.  A brain computer interface-based explorer , 2015, Journal of Neuroscience Methods.

[11]  Benjamin Blankertz,et al.  Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP) , 2013, Clinical Neurophysiology.

[12]  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..

[13]  Xingyu Wang,et al.  Towards correlation-based time window selection method for motor imagery BCIs , 2018, Neural Networks.

[14]  G. Riva,et al.  The use of P300-based BCIs in amyotrophic lateral sclerosis: from augmentative and alternative communication to cognitive assessment , 2012, Brain and behavior.

[15]  N. Birbaumer,et al.  Self-regulation of slow cortical potentials in completely paralyzed human patients , 1998, Neuroscience Letters.

[16]  Miguel A L Nicolelis,et al.  Electrical stimulation of the dorsal columns of the spinal cord for Parkinson's disease , 2017, Movement disorders : official journal of the Movement Disorder Society.

[17]  Andrea Kübler,et al.  Independent home use of Brain Painting improves quality of life of two artists in the locked-in state diagnosed with amyotrophic lateral sclerosis , 2015 .

[18]  A. Kübler,et al.  Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials , 2014, Journal of NeuroEngineering and Rehabilitation.

[19]  Xingyu Wang,et al.  Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface , 2015, Journal of Neuroscience Methods.

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

[21]  J. Wolpaw,et al.  P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls , 2015, Clinical Neurophysiology.

[22]  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.

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

[24]  Yuanqing Li,et al.  An asynchronous wheelchair control by hybrid EEG–EOG brain–computer interface , 2014, Cognitive Neurodynamics.

[25]  Xingyu Wang,et al.  Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis , 2013, Int. J. Neural Syst..

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

[27]  Yodchanan Wongsawat,et al.  Navigation-synchronized multimodal control wheelchair from brain to alternative assistive technologies for persons with severe disabilities , 2017, Cognitive Neurodynamics.

[28]  Xingyu Wang,et al.  An ERP-Based BCI using an oddball Paradigm with Different Faces and Reduced errors in Critical Functions , 2014, Int. J. Neural Syst..

[29]  F. Cincotti,et al.  Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis , 2013, Front. Hum. Neurosci..

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

[31]  Yangsong Zhang,et al.  Robust frequency recognition for SSVEP-based BCI with temporally local multivariate synchronization index , 2016, Cognitive Neurodynamics.

[32]  R Ron-Angevin,et al.  Initial test of a T9-like P300-based speller by an ALS patient , 2015, Journal of neural engineering.

[33]  J. Wolpaw,et al.  Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis , 2014, Amyotrophic lateral sclerosis & frontotemporal degeneration.

[34]  Xingyu Wang,et al.  An exploration of spatial auditory BCI paradigms with different sounds: music notes versus beeps , 2016, Cognitive Neurodynamics.

[35]  Xingyu Wang,et al.  An adaptive P300-based control system , 2011, Journal of neural engineering.

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

[37]  Wei Wu,et al.  Multimodal BCIs: Target Detection, Multidimensional Control, and Awareness Evaluation in Patients With Disorder of Consciousness , 2016, Proceedings of the IEEE.

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

[39]  A. Cichocki,et al.  An optimized ERP brain–computer interface based on facial expression changes , 2014, Journal of neural engineering.

[40]  B O Mainsah,et al.  Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS study , 2015, Journal of neural engineering.

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

[42]  K. Priftis,et al.  Brain–computer interfaces in amyotrophic lateral sclerosis: A metanalysis , 2015, Clinical Neurophysiology.

[43]  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.

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

[45]  Andrzej Cichocki,et al.  An improved P300 pattern in BCI to catch user’s attention , 2017, Journal of neural engineering.

[46]  C. Neuper,et al.  Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges , 2010, Front. Neurosci..

[47]  Jan Kassubek,et al.  Eye-tracking controlled cognitive function tests in patients with amyotrophic lateral sclerosis: a controlled proof-of-principle study , 2015, Journal of Neurology.

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

[49]  Tzyy-Ping Jung,et al.  A Brain–Computer Interface Based on Miniature-Event-Related Potentials Induced by Very Small Lateral Visual Stimuli , 2018, IEEE Transactions on Biomedical Engineering.

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

[51]  J. Wolpaw,et al.  EEG correlates of P300-based brain–computer interface (BCI) performance in people with amyotrophic lateral sclerosis , 2012, Journal of neural engineering.

[52]  Xingyu Wang,et al.  A P300 Brain-Computer Interface Based on a Modification of the Mismatch Negativity Paradigm , 2015, Int. J. Neural Syst..

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

[54]  Javier Gomez-Pilar,et al.  An Asynchronous P300-Based Brain-Computer Interface Web Browser for Severely Disabled People , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[55]  Michele Lewis,et al.  The role of physical therapy and occupational therapy in the treatment of amyotrophic lateral sclerosis. , 2007, NeuroRehabilitation.

[56]  R John Leigh,et al.  Eye movements in amyotrophic lateral sclerosis and its mimics: a review with illustrative cases , 2010, Journal of Neurology, Neurosurgery & Psychiatry.