Investigating the effects of a sensorimotor rhythm-based BCI training on the cortical activity elicited by mental imagery

OBJECTIVE It is well known that to acquire sensorimotor (SMR)-based brain-computer interface (BCI) control requires a training period before users can achieve their best possible performances. Nevertheless, the effect of this training procedure on the cortical activity related to the mental imagery ability still requires investigation to be fully elucidated. The aim of this study was to gain insights into the effects of SMR-based BCI training on the cortical spectral activity associated with the performance of different mental imagery tasks. APPROACH Linear cortical estimation and statistical brain mapping techniques were applied on high-density EEG data acquired from 18 healthy participants performing three different mental imagery tasks. Subjects were divided in two groups, one of BCI trained subjects, according to their previous exposure (at least six months before this study) to motor imagery-based BCI training, and one of subjects who were naive to any BCI paradigms. MAIN RESULTS Cortical activation maps obtained for trained and naive subjects indicated different spectral and spatial activity patterns in response to the mental imagery tasks. Long-term effects of the previous SMR-based BCI training were observed on the motor cortical spectral activity specific to the BCI trained motor imagery task (simple hand movements) and partially generalized to more complex motor imagery task (playing tennis). Differently, mental imagery with spatial attention and memory content could elicit recognizable cortical spectral activity even in subjects completely naive to (BCI) training. SIGNIFICANCE The present findings contribute to our understanding of BCI technology usage and might be of relevance in those clinical conditions when training to master a BCI application is challenging or even not possible.

[1]  M. Jeannerod.  The representing brain: Neural correlates of motor intention and imagery , 1994, Behavioral and Brain Sciences.

[2]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[3]  Steven Laureys,et al.  When thoughts become action: An fMRI paradigm to study volitional brain activity in non-communicative brain injured patients , 2007, NeuroImage.

[4]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[5]  G. Pfurtscheller,et al.  Information transfer rate in a five-classes brain-computer interface , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Ian Daly,et al.  On the control of brain-computer interfaces by users with cerebral palsy , 2013, Clinical Neurophysiology.

[7]  R. Lesser,et al.  Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. I. Alpha and beta event-related desynchronization. , 1998, Brain : a journal of neurology.

[8]  Martin Lotze,et al.  Kinesthetic imagery of musical performance , 2013, Front. Hum. Neurosci..

[9]  H. Siebner,et al.  Effector‐independent representations of simple and complex imagined finger movements: a combined fMRI and TMS study , 2003, The European journal of neuroscience.

[10]  E. Paulesu,et al.  Mental images across the adult lifespan: a behavioural and fMRI investigation of motor execution and motor imagery , 2012, Experimental Brain Research.

[11]  S. Makeig,et al.  Human Brain Dynamics Accompanying Use of Egocentric and Allocentric Reference Frames during Navigation , 2010, Journal of Cognitive Neuroscience.

[12]  G. Pfurtscheller,et al.  Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. , 1979, Electroencephalography and clinical neurophysiology.

[13]  S. Bricogne,et al.  Neural Correlates of Topographic Mental Exploration: The Impact of Route versus Survey Perspective Learning , 2000, NeuroImage.

[14]  Marco Congedo,et al.  Brain Oscillatory Activity during Spatial Navigation: Theta and Gamma Activity Link Medial Temporal and Parietal Regions , 2012, Journal of Cognitive Neuroscience.

[15]  Per Christian Hansen,et al.  Analysis of Discrete Ill-Posed Problems by Means of the L-Curve , 1992, SIAM Rev..

[16]  G. Pfurtscheller,et al.  Patterns of cortical activation during planning of voluntary movement. , 1989, Electroencephalography and clinical neurophysiology.

[17]  F. Babiloni,et al.  Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function , 2005, NeuroImage.

[18]  M Corbetta,et al.  Frontoparietal cortical networks for directing attention and the eye to visual locations: identical, independent, or overlapping neural systems? , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[19]  F. Carver,et al.  Human Hippocampal and Parahippocampal Theta during Goal-Directed Spatial Navigation Predicts Performance on a Virtual Morris Water Maze , 2008, The Journal of Neuroscience.

[20]  B. Costello,et al.  The human volatilome: volatile organic compounds (VOCs) in exhaled breath, skin emanations, urine, feces and saliva , 2014, Journal of breath research.

[21]  V. Jousmäki,et al.  Involvement of Primary Motor Cortex in Motor Imagery: A Neuromagnetic Study , 1997, NeuroImage.

[22]  W. Klimesch.  EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[23]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[24]  C. Neuper,et al.  The effect of distinct mental strategies on classification performance for brain-computer interfaces. , 2012, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[25]  C. Neuper,et al.  Sensorimotor rhythm-based brain–computer interface training: the impact on motor cortical responsiveness , 2011, Journal of neural engineering.

[26]  Sven Hoffmann,et al.  The Correction of Eye Blink Artefacts in the EEG: A Comparison of Two Prominent Methods , 2008, PloS one.

[27]  Jonathan R. Wolpaw,et al.  Brain–Computer InterfacesPrinciples and Practice , 2012 .

[28]  G. Oriolo,et al.  Non-invasive brain–computer interface system: Towards its application as assistive technology , 2008, Brain Research Bulletin.

[29]  J. Wolpaw,et al.  Brain-Computer Interfaces: Principles and Practice , 2012 .

[30]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

[31]  Laura Astolfi,et al.  Multimodal integration of EEG, MEG and fMRI data for the solution of the neuroimage puzzle. , 2004, Magnetic resonance imaging.

[32]  Vera Kaiser,et al.  Hybrid brain-computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury , 2013, Artif. Intell. Medicine.

[33]  Alan C. Evans,et al.  Enhancement of MR Images Using Registration for Signal Averaging , 1998, Journal of Computer Assisted Tomography.

[34]  W. A. Sarnacki,et al.  Brain–computer interface (BCI) operation: optimizing information transfer rates , 2003, Biological Psychology.

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

[36]  Klaus-Robert Müller,et al.  Neurophysiological predictor of SMR-based BCI performance , 2010, NeuroImage.

[37]  F Cincotti,et al.  Linear inverse source estimate of combined EEG and MEG data related to voluntary movements , 2001, Human brain mapping.

[38]  G Pfurtscheller,et al.  Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[39]  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.

[40]  Klaus-Robert Müller,et al.  The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects , 2008, IEEE Transactions on Biomedical Engineering.

[41]  M. Diamond,et al.  Primary Motor and Sensory Cortex Activation during Motor Performance and Motor Imagery: A Functional Magnetic Resonance Imaging Study , 1996, The Journal of Neuroscience.

[42]  M. Jeannerod,et al.  Mental imaging of motor activity in humans , 1999, Current Opinion in Neurobiology.

[43]  Klaus-Robert Müller,et al.  Introduction to machine learning for brain imaging , 2011, NeuroImage.

[44]  N. Birbaumer,et al.  Predictability of Brain-Computer Communication , 2004 .

[45]  S. Cochin,et al.  Observation and execution of movement: similarities demonstrated by quantified electroencephalography , 1999, The European journal of neuroscience.

[46]  S. Raghavachari,et al.  Distinct patterns of brain oscillations underlie two basic parameters of human maze learning. , 2001, Journal of neurophysiology.

[47]  Jukka Heikkonen,et al.  A local neural classifier for the recognition of EEG patterns associated to mental tasks , 2002, IEEE Trans. Neural Networks.

[48]  Leslie G. Ungerleider,et al.  An area specialized for spatial working memory in human frontal cortex. , 1998, Science.

[49]  Rajesh P. N. Rao,et al.  Cortical activity during motor execution, motor imagery, and imagery-based online feedback , 2010, Proceedings of the National Academy of Sciences.

[50]  B. Milner,et al.  Right hippocampal impairment in the recall of spatial location: Encoding deficit or rapid forgetting? , 1989, Neuropsychologia.

[51]  Klaus-Robert Müller,et al.  The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.

[52]  Reinhold Scherer,et al.  A fully on-line adaptive BCI , 2006, IEEE Transactions on Biomedical Engineering.

[53]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .