Wireless EEG with individualized channel layout enables efficient motor imagery training

OBJECTIVE The study compared two channel-reduction approaches in order to investigate the effects of systematic motor imagery (MI) neurofeedback practice in an everyday environment using a very user-friendly EEG system consisting of individualized caps and highly portable hardware. METHODS Sixteen BCI novices were trained over four consecutive days to imagine left and right hand movements while receiving feedback. The most informative bipolar channels for use on the subsequent days were identified on the first day for each individual based on a high-density online MI recording. RESULTS Online classification accuracy on the first day was 85.1% on average (range: 64.7-97.7%). Offline an individually-selected bipolar channel pair based on common spatial patterns significantly outperformed a pair informed by independent component analysis and a standard 10-20 pair. From day 2 to day 4 online MI accuracy increased significantly (day 2: 69.1%; day 4: 73.3%), which was mostly caused by a reduction in ipsilateral event-related desynchronization of sensorimotor rhythms. CONCLUSION The present study demonstrates that systematic MI practice in an everyday environment with a user-friendly EEG system results in MI learning effects. SIGNIFICANCE These findings help to bridge the gap between elaborate laboratory studies with healthy participants and efficient home or hospital based MI neurofeedback protocols.

[1]  T. Jung,et al.  Translation of EEG Spatial Filters from Resting to Motor Imagery Using Independent Component Analysis , 2012, PloS one.

[2]  K. Wong,et al.  Factors Associated with Severity on Admission and In-Hospital Mortality after Primary Intracerebral Hemorrhage in China , 2013, International journal of stroke : official journal of the International Stroke Society.

[3]  Maarten De Vos,et al.  Let's face it, from trial to trial: Comparing procedures for N170 single-trial estimation , 2012, NeuroImage.

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

[5]  W. K. Simmons,et al.  Circular analysis in systems neuroscience: the dangers of double dipping , 2009, Nature Neuroscience.

[6]  Maarten De Vos,et al.  Semi-automatic attenuation of cochlear implant artifacts for the evaluation of late auditory evoked potentials , 2012, Hearing Research.

[7]  L. Cohen,et al.  Reorganization of the human ipsilesional premotor cortex after stroke. , 2004, Brain : a journal of neurology.

[8]  W. A. Sarnacki,et al.  Electroencephalographic (EEG) control of three-dimensional movement , 2010, Journal of neural engineering.

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

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

[11]  J. Wolpaw,et al.  Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements , 2004, Brain Topography.

[12]  C. Neuper,et al.  Long-term evaluation of a 4-class imagery-based brain–computer interface , 2013, Clinical Neurophysiology.

[13]  Lara A. Boyd,et al.  The Kinesthetic and Visual Imagery Questionnaire Is a Reliable Tool for Individuals With Parkinson Disease , 2010, Journal of neurologic physical therapy : JNPT.

[14]  G. Pfurtscheller,et al.  Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain–computer interface , 2009, Clinical Neurophysiology.

[15]  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.

[16]  D. Cheyne.  MEG studies of sensorimotor rhythms: A review , 2013, Experimental Neurology.

[17]  P. Langhorne,et al.  Motor recovery after stroke: a systematic review , 2009, The Lancet Neurology.

[18]  B. Schölkopf,et al.  An online brain-computer interface based on shifting attention to concurrent streams of auditory stimuli. , 2012, Journal of neural engineering.

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

[20]  J. Ushiba,et al.  Daily training with realistic visual feedback improves reproducibility of event-related desynchronisation following hand motor imagery , 2013, Clinical Neurophysiology.

[21]  N. Takeuchi,et al.  Maladaptive Plasticity for Motor Recovery after Stroke: Mechanisms and Approaches , 2012, Neural plasticity.

[22]  Guillaume Gibert,et al.  OpenViBE: An Open-Source Software Platform to Design, Test, and Use BrainComputer Interfaces in Real and Virtual Environments , 2010, PRESENCE: Teleoperators and Virtual Environments.

[23]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[24]  C. Richards,et al.  The Kinesthetic and Visual Imagery Questionnaire (KVIQ) for Assessing Motor Imagery in Persons with Physical Disabilities: A Reliability and Construct Validity Study , 2007, Journal of neurologic physical therapy : JNPT.

[25]  R. C. Oldfield.  The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[26]  M. Jeannerod.  Mental imagery in the motor context , 1995, Neuropsychologia.

[27]  Bin He,et al.  EEG Control of a Virtual Helicopter in 3-Dimensional Space Using Intelligent Control Strategies , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  G. Pfurtscheller,et al.  EEG-based discrimination between imagination of right and left hand movement. , 1997, Electroencephalography and clinical neurophysiology.

[29]  Febo Cincotti,et al.  EEG-based Brain-Computer Interface to support post-stroke motor rehabilitation of the upper limb , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[30]  Rupert Ortner,et al.  A Motor Imagery Based Brain-Computer Interface for Stroke Rehabilitation , 2012, Annual Review of Cybertherapy and Telemedicine.

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

[32]  Yuanqing Li,et al.  Classifying Motor Imagery EEG Signals by Iterative Channel Elimination according to Compound Weight , 2010, AICI.

[33]  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.

[34]  Tobias Kaufmann,et al.  Spelling is Just a Click Away – A User-Centered Brain–Computer Interface Including Auto-Calibration and Predictive Text Entry , 2012, Front. Neurosci..

[35]  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.

[36]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[37]  Stefan Debener,et al.  Uncovering auditory evoked potentials from cochlear implant users with independent component analysis. , 2011, Psychophysiology.

[38]  Clemens Brunner,et al.  Dimensionality Reduction and Channel Selection of Motor Imagery Electroencephalographic Data , 2009, Comput. Intell. Neurosci..

[39]  G. Pfurtscheller,et al.  Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. , 2005, Brain research. Cognitive brain research.

[40]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[41]  K.-R. Muller,et al.  Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.

[42]  M. Jeannerod.  Neural Simulation of Action: A Unifying Mechanism for Motor Cognition , 2001, NeuroImage.

[43]  Tom Eichele,et al.  Semi-automatic identification of independent components representing EEG artifact , 2009, Clinical Neurophysiology.

[44]  C. Richards,et al.  Clinical Assessment of Motor Imagery After Stroke , 2008, Neurorehabilitation and neural repair.

[45]  Bernhard Schölkopf,et al.  Robust EEG Channel Selection across Subjects for Brain-Computer Interfaces , 2005, EURASIP J. Adv. Signal Process..

[46]  Till R. Schneider,et al.  Using ICA for the Analysis of Multi-Channel EEG Data , 2010 .

[47]  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.

[48]  Christian Grefkes,et al.  Cortical Reorganization After Stroke , 2014, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[49]  G. Prasad,et al.  Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study , 2010, Journal of NeuroEngineering and Rehabilitation.

[50]  Selina Wriessnegger,et al.  Regularised CSP for Sensor Selection in BCI , 2006 .

[51]  C. Schuster,et al.  Best practice for motor imagery: a systematic literature review on motor imagery training elements in five different disciplines , 2011, BMC medicine.

[52]  Xiaorong Gao,et al.  Bipolar electrode selection for a motor imagery based brain–computer interface , 2008, Journal of neural engineering.

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

[54]  Xiaorong Gao,et al.  Design of electrode layout for motor imagery based brain--computer interface , 2007 .

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

[56]  G. Pfurtscheller,et al.  Designing optimal spatial filters for single-trial EEG classification in a movement task , 1999, Clinical Neurophysiology.

[57]  Ethan R. Buch,et al.  Think to Move: a Neuromagnetic Brain-Computer Interface (BCI) System for Chronic Stroke , 2008, Stroke.

[58]  Jyh-Yeong Chang,et al.  Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors , 2012, Journal of NeuroEngineering and Rehabilitation.

[59]  H. Jasper,et al.  Electrocorticograms in man: Effect of voluntary movement upon the electrical activity of the precentral gyrus , 1949 .

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

[61]  G. Pfurtscheller,et al.  Enhancement of left-right sensorimotor EEG differences during feedback-regulated motor imagery. , 1999, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[62]  T. Hinterberger,et al.  Automated EEG feature selection for brain computer interfaces , 2003, First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings..

[63]  Arnaud Delorme,et al.  EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing , 2011, Comput. Intell. Neurosci..

[64]  Yijun Wang,et al.  Implementation of a Brain-Computer Interface Based on Three States of Motor Imagery , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[65]  C. Braun,et al.  Chronic stroke recovery after combined BCI training and physiotherapy: a case report. , 2011, Psychophysiology.

[66]  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.

[67]  Yijun Wang,et al.  Phase Synchrony Measurement in Motor Cortex for Classifying Single-trial EEG during Motor Imagery , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[68]  José del R. Millán,et al.  Brain-actuated interaction , 2004, Artif. Intell..

[69]  Marcel Zeelenberg,et al.  Data analysis 2 , 2016 .

[70]  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.

[71]  G. Pfurtscheller,et al.  Conversion of EEG activity into cursor movement by a brain-computer interface (BCI) , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[72]  Tzyy-Ping Jung,et al.  Dry-Contact and Noncontact Biopotential Electrodes: Methodological Review , 2010, IEEE Reviews in Biomedical Engineering.

[73]  Corina Schuster,et al.  Two assessments to evaluate imagery ability: translation, test-retest reliability and concurrent validity of the German KVIQ and Imaprax , 2012, BMC Medical Research Methodology.

[74]  P. Matthews,et al.  The role of ipsilateral premotor cortex in hand movement after stroke , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[75]  Rajesh P. N. Rao,et al.  Towards adaptive classification for BCI , 2006, Journal of neural engineering.

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

[77]  S. Debener,et al.  Towards a truly mobile auditory brain-computer interface: exploring the P300 to take away. , 2014, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[78]  K. Müller,et al.  Single Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes , 2007, PloS one.

[79]  K.-R. Muller,et al.  The Berlin brain-computer interface: EEG-based communication without subject training , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.