Nonspecific Visuospatial Imagery as a Novel Mental Task for Online EEG-Based BCI Control

Brain-computer interfaces (BCIs) can provide a means of communication to individuals with severe motor disorders, such as those presenting as locked-in. Many BCI paradigms rely on motor neural pathways, which are often impaired in these individuals. However, recent findings suggest that visuospatial function may remain intact. This study aimed to determine whether visuospatial imagery, a previously unexplored task, could be used to signify intent in an online electroencephalography (EEG)-based BCI. Eighteen typically developed participants imagined checkerboard arrow stimuli in four quadrants of the visual field in 5-s trials, while signals were collected using 16 dry electrodes over the visual cortex. In online blocks, participants received graded visual feedback based on their performance. An initial BCI pipeline (visuospatial imagery classifier I) attained a mean accuracy of [Formula: see text]% classifying rest against visuospatial imagery in online trials. This BCI pipeline was further improved using restriction to alpha band features (visuospatial imagery classifier II), resulting in a mean pseudo-online accuracy of [Formula: see text]%. Accuracies exceeded the threshold for practical BCIs in 12 participants. This study supports the use of visuospatial imagery as a real-time, binary EEG-BCI control paradigm.

[1]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

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

[3]  M. V. Gerven,et al.  Attention modulations of posterior alpha as a control signal for two-dimensional brain–computer interfaces , 2009, Journal of Neuroscience Methods.

[4]  Karim Jerbi,et al.  Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy , 2015, Journal of Neuroscience Methods.

[5]  Phillip J. Moore,et al.  Verbal and visual learning styles , 1988 .

[6]  D. Ulrich,et al.  A Gaze Independent Brain-Computer Interface Based on Visual Stimulation through Closed Eyelids , 2015, Scientific Reports.

[7]  Lynne V. Gauthier,et al.  Gross motor ability predicts response to upper extremity rehabilitation in chronic stroke , 2017, Behavioural Brain Research.

[8]  Jonathan R Wolpaw,et al.  Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis , 2018, Neurology.

[9]  Guillaume A. Rousselet,et al.  Single-trial EEG dynamics of object and face visual processing , 2007, NeuroImage.

[10]  S. Bozinovski,et al.  Using EEG alpha rhythm to control a mobile robot , 1988, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  L Tonin,et al.  An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation , 2013, Journal of neural engineering.

[12]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[13]  N. Birbaumer,et al.  Brain–computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralysed patients? , 2008, Clinical Neurophysiology.

[14]  J J Vidal,et al.  Toward direct brain-computer communication. , 1973, Annual review of biophysics and bioengineering.

[15]  Ali Bahramisharif,et al.  Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention , 2011, Journal of NeuroEngineering and Rehabilitation.

[16]  Arnold Wilkins,et al.  Wind turbines, flicker, and photosensitive epilepsy: Characterizing the flashing that may precipitate seizures and optimizing guidelines to prevent them , 2008, Epilepsia.

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

[18]  G. Bottini,et al.  Exploring motor and visual imagery in Amyotrophic Lateral Sclerosis , 2013, Experimental Brain Research.

[19]  Lynne V. Gauthier,et al.  Computer-aided prediction of extent of motor recovery following constraint-induced movement therapy in chronic stroke , 2017, Behavioural Brain Research.

[20]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[21]  P. Osterrieth.  Le test de copie d'une figure complexe , 1944 .

[22]  J. Buford,et al.  Brain–Computer Interface after Nervous System Injury , 2014, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[23]  H. Adeli,et al.  Brain-computer interface technologies: from signal to action , 2013, Reviews in the neurosciences.

[24]  Christian Kothe,et al.  Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general , 2011, Journal of neural engineering.

[25]  M. Delargy,et al.  Locked-in syndrome , 2005, BMJ : British Medical Journal.

[26]  Michael Schrauf,et al.  Alpha spindles as neurophysiological correlates indicating attentional shift in a simulated driving task. , 2012, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[27]  N. Kanwisher,et al.  The lateral occipital complex and its role in object recognition , 2001, Vision Research.

[28]  S. Kosslyn,et al.  Dissociation between visual attention and visual mental imagery , 2011 .

[29]  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.

[30]  W. Klimesch.  Alpha-band oscillations, attention, and controlled access to stored information , 2012, Trends in Cognitive Sciences.

[31]  T. Chau,et al.  Weaning Off Mental Tasks to Achieve Voluntary Self-Regulatory Control of a Near-Infrared Spectroscopy Brain-Computer Interface , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[32]  Marieke E. Thurlings,et al.  Gaze-independent ERP-BCIs: augmenting performance through location-congruent bimodal stimuli , 2014, Front. Syst. Neurosci..

[33]  M. Viergever,et al.  Real-time decoding of the direction of covert visuospatial attention. , 2012, Journal of neural engineering.

[34]  Tom Chau,et al.  Feature clustering for robust frequency-domain classification of EEG activity , 2016, Journal of Neuroscience Methods.

[35]  Á. Pascual-Leone,et al.  α-Band Electroencephalographic Activity over Occipital Cortex Indexes Visuospatial Attention Bias and Predicts Visual Target Detection , 2006, The Journal of Neuroscience.

[36]  N. Birbaumer,et al.  Brain–computer interfaces for communication and rehabilitation , 2016, Nature Reviews Neurology.

[37]  T. Chau,et al.  Effects of user mental state on EEG-BCI performance , 2015, Front. Hum. Neurosci..

[38]  J. Buford,et al.  Combined corticospinal and reticulospinal effects on upper limb muscles , 2014, Neuroscience Letters.

[39]  H. Aurlien,et al.  EEG background activity described by a large computerized database , 2004, Clinical Neurophysiology.

[40]  Elaine Astrand,et al.  Selective visual attention to drive cognitive brain–machine interfaces: from concepts to neurofeedback and rehabilitation applications , 2014, Front. Syst. Neurosci..

[41]  S. Kosslyn,et al.  Visual mental imagery induces retinotopically organized activation of early visual areas. , 2005, Cerebral cortex.

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

[43]  Stevo Bozinovski,et al.  Brain–Computer Interface in Europe: the thirtieth anniversary , 2019 .

[44]  J. Buford,et al.  Wavelet methodology to improve single unit isolation in primary motor cortex cells , 2015, Journal of Neuroscience Methods.

[45]  Leslie G. Ungerleider,et al.  Distributed Neural Systems for the Generation of Visual Images , 2000, Neuron.

[46]  Dandan Huang,et al.  Towards a user-friendly brain–computer interface: Initial tests in ALS and PLS patients , 2010, Clinical Neurophysiology.

[47]  M. J. Emerson,et al.  The Unity and Diversity of Executive Functions and Their Contributions to Complex “Frontal Lobe” Tasks: A Latent Variable Analysis , 2000, Cognitive Psychology.

[48]  Xin Zhao,et al.  Visual attention recognition based on nonlinear dynamical parameters of EEG. , 2014, Bio-medical materials and engineering.

[49]  Gernot R. Müller-Putz,et al.  Discrimination of Motor Imagery-Induced EEG Patterns in Patients with Complete Spinal Cord Injury , 2009, Comput. Intell. Neurosci..

[50]  Tom Chau,et al.  A Passive EEG-BCI for Single-Trial Detection of Changes in Mental State , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[51]  D. Kennard,et al.  Perceptual suppression of afterimages. , 1970, Vision research.

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

[53]  Monica-Claudia Dobrea,et al.  The selection of proper discriminative cognitive tasks — A necessary prerequisite in high-quality BCI applications , 2009, 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies.

[54]  Ellen S. Wilschut,et al.  Brain–machine interfaces in space: Using spontaneous rather than intentionally generated brain signals , 2010 .

[55]  Tom Chau,et al.  Exploring methodological frameworks for a mental task-based near-infrared spectroscopy brain–computer interface , 2015, Journal of Neuroscience Methods.

[56]  Steven Yantis,et al.  Efficient acquisition of human retinotopic maps , 2003, Human brain mapping.

[57]  Sharon L Thompson-Schill,et al.  The Neural Correlates of Visual and Verbal Cognitive Styles , 2009, The Journal of Neuroscience.

[58]  Michael Schrauf,et al.  EEG alpha spindle measures as indicators of driver fatigue under real traffic conditions , 2011, Clinical Neurophysiology.

[59]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[60]  Max A. Viergever,et al.  BCI control using 4 direction spatial visual attention and real-time fMRI at 7T , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[61]  John J. Foxe,et al.  The Role of Alpha-Band Brain Oscillations as a Sensory Suppression Mechanism during Selective Attention , 2011, Front. Psychology.

[62]  Dong Ming,et al.  Research on Visual Attention Classification Based on EEG Entropy Parameters , 2013 .

[63]  Jussi T. Lindgren,et al.  Attending to Visual Stimuli versus Performing Visual Imagery as a Control Strategy for EEG-based Brain-Computer Interfaces , 2018, Scientific Reports.

[64]  A. Frolov,et al.  Brain-Computer Interface Based on Generation of Visual Images , 2011, PloS one.

[65]  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.

[66]  J. del R. Millán,et al.  Time-dependent approach for single trial classification of covert visuospatial attention , 2012, Journal of neural engineering.