Expanding the (kaleido)scope: exploring current literature trends for translating electroencephalography (EEG) based brain–computer interfaces for motor rehabilitation in children

Rehabilitation applications using brain-computer interfaces (BCI) have recently shown encouraging results for motor recovery. Effective BCI neurorehabilitation has been shown to exploit neuroplastic properties of the brain through mental imagery tasks. However, these applications and results are currently restricted to adults. A systematic search reveals there is essentially no literature describing motor rehabilitative BCI applications that use electroencephalograms (EEG) in children, despite advances in such applications with adults. Further inspection highlights limited literature pursuing research in the field, especially outside of neurofeedback paradigms. Then the question naturally arises, do current literature trends indicate that EEG based BCI motor rehabilitation applications could be translated to children? To provide further evidence beyond the available literature for this particular topic, we present an exploratory survey examining some of the indirect literature related to motor rehabilitation BCI in children. Our goal is to establish if evidence in the related literature supports research on this topic and if the related studies can help explain the dearth of current research in this area. The investigation found positive literature trends in the indirect studies which support translating these BCI applications to children and provide insight into potential pitfalls perhaps responsible for the limited literature. Careful consideration of these pitfalls in conjunction with support from the literature emphasize that fully realized motor rehabilitation BCI applications for children are feasible and would be beneficial. •  BCI intervention has improved motor recovery in adult patients and offer supplementary rehabilitation options to patients. •  A systematic literature search revealed that essentially no research has been conducted bringing motor rehabilitation BCI applications to children, despite advances in BCI. •  Indirect studies discovered from the systematic literature search, i.e. neurorehabilitation in children via BCI for autism spectrum disorder, provide insight into translating motor rehabilitation BCI applications to children. •  Translating BCI applications to children is a relevant, important area of research which is relatively barren.

[1]  Brendan Z. Allison,et al.  Could Anyone Use a BCI? , 2010, Brain-Computer Interfaces.

[2]  F. D. Rose,et al.  Virtual enriched environments in paediatric neuropsychological rehabilitation following traumatic brain injury: Feasibility, benefits and challenges , 2009, Developmental neurorehabilitation.

[3]  C. Grozea,et al.  Bristle-sensors—low-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications , 2011, Journal of neural engineering.

[4]  Sacha Jennifer van Albada,et al.  Age trends and sex differences of alpha rhythms including split alpha peaks , 2011, Clinical Neurophysiology.

[5]  Dirk Heylen,et al.  Brain-Computer Interfacing and Games , 2010, Brain-Computer Interfaces.

[6]  N. Fox,et al.  Development of the EEG from 5 months to 4 years of age , 2002, Clinical Neurophysiology.

[7]  Marc M. Van Hulle,et al.  Language Model Applications to Spelling with Brain-Computer Interfaces , 2014, Sensors.

[8]  H. Zhang,et al.  A tensor-based scheme for stroke patients’ motor imagery EEG analysis in BCI-FES rehabilitation training , 2014, Journal of Neuroscience Methods.

[9]  Desney S. Tan,et al.  Brain-Computer Interfaces and Human-Computer Interaction , 2010, Brain-Computer Interfaces.

[10]  Andrzej Cichocki,et al.  Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis , 2014, IEEE Signal Processing Magazine.

[11]  Juliana Yordanova,et al.  Analysis of phase-locking is informative for studying event-related EEG activity , 1997, Biological Cybernetics.

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

[13]  Liqing Zhang,et al.  A Boosting-Based Spatial-Spectral Model for Stroke Patients’ EEG Analysis in Rehabilitation Training , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  M Matsuura,et al.  Age development and sex differences of various EEG elements in healthy children and adults--quantification by a computerized wave form recognition method. , 1985, Electroencephalography and clinical neurophysiology.

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

[16]  Gernot R. Müller-Putz,et al.  Exploration of the neural correlates of cerebral palsy for sensorimotor BCI control , 2014, Front. Neuroeng..

[17]  Daniel P. Ferris,et al.  How Many Electrodes Are Really Needed for EEG-Based Mobile Brain Imaging? , 2012 .

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

[19]  Robert Minahan,et al.  Language recovery after left hemispherectomy in children with late‐onset seizures , 1999, Annals of neurology.

[20]  Xingyu Wang,et al.  Removal of EEG artifacts for BCI applications using fully Bayesian tensor completion , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[21]  Dennis J. McFarland,et al.  Brain-computer interface (BCI) operation: signal and noise during early training sessions , 2005, Clinical Neurophysiology.

[22]  Chang S. Nam,et al.  The Human Factors and Ergonomics of P300-Based Brain-Computer Interfaces , 2015, Brain sciences.

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

[24]  A Graser,et al.  BCI Demographics II: How Many (and What Kinds of) People Can Use a High-Frequency SSVEP BCI? , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[25]  Jie Li,et al.  EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme , 2016, Comput. Intell. Neurosci..

[26]  Geoffrey P. Bingham,et al.  A Sensorimotor Approach to the Training of Manual Actions in Children With Developmental Coordination Disorder , 2013, Journal of child neurology.

[27]  Dariusz Mikołajewski,et al.  The prospects of brain — computer interface applications in children , 2014 .

[28]  Michelle K. Jetha,et al.  Electrophysiological changes during adolescence: A review , 2010, Brain and Cognition.

[29]  Jonathan R Wolpaw,et al.  Brain–computer interfaces as new brain output pathways , 2007, The Journal of physiology.

[30]  Cuntai Guan,et al.  A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke , 2015, Clinical EEG and neuroscience.

[31]  Austin J. Brockmeier,et al.  Joint optimization of algorithmic suites for EEG analysis , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  Michael L. Boninger,et al.  Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays , 2015, Front. Integr. Neurosci..

[33]  Alireza Gharabaghi,et al.  Brain–robot interface driven plasticity: Distributed modulation of corticospinal excitability , 2016, NeuroImage.

[34]  Sung Chan Jun,et al.  A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users , 2014, Sensors.

[35]  Niels Birbaumer,et al.  Abnormal Neural Connectivity in Schizophrenia and fMRI-Brain-Computer Interface as a Potential Therapeutic Approach , 2012, Front. Psychiatry.

[36]  Harold Bekkering,et al.  Neural Evidence for Compromised Motor Imagery in Right Hemiparetic Cerebral Palsy , 2010, Front. Neur..

[37]  A. Gordon,et al.  Motor learning of a bimanual task in children with unilateral cerebral palsy. , 2013, Research in developmental disabilities.

[38]  Moritz Grosse-Wentrup,et al.  Using brain–computer interfaces to induce neural plasticity and restore function , 2011, Journal of neural engineering.

[39]  Simon McCarthy-Jones,et al.  Taking back the brain: could neurofeedback training be effective for relieving distressing auditory verbal hallucinations in patients with schizophrenia? , 2012, Schizophrenia bulletin.

[40]  R. Boyd,et al.  Systematic review of physiotherapy interventions to improve gross motor capacity and performance in children and adolescents with an acquired brain injury , 2016, Brain injury.

[41]  B. Allison,et al.  BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI? , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[42]  Diana Valbuena,et al.  Age-Specific Mechanisms in an SSVEP-Based BCI Scenario: Evidences from Spontaneous Rhythms and Neuronal Oscillators , 2012, Comput. Intell. Neurosci..

[43]  Ruben C. Gur,et al.  Cognitive and neural strategies during control of the anterior cingulate cortex by fMRI neurofeedback in patients with schizophrenia , 2015, Front. Behav. Neurosci..

[44]  B. Thomas,et al.  The efficacy of playing a virtual reality game in modulating pain for children with acute burn injuries: A randomized controlled trial [ISRCTN87413556] , 2005, BMC pediatrics.

[45]  Maurizio Corbetta,et al.  The effect of age on human motor electrocorticographic signals and implications for brain–computer interface applications , 2011, Journal of neural engineering.

[46]  Michael V. Johnston,et al.  Clinical disorders of brain plasticity , 2004, Brain and Development.

[47]  Ravi S. Menon,et al.  Resting State and Diffusion Neuroimaging Predictors of Clinical Improvements Following Constraint-Induced Movement Therapy in Children With Hemiplegic Cerebral Palsy , 2015, Journal of child neurology.

[48]  Vivek Prabhakaran,et al.  Changes in functional brain organization and behavioral correlations after rehabilitative therapy using a brain-computer interface , 2014, Front. Neuroeng..

[49]  T. Chau,et al.  A Review of EEG-Based Brain-Computer Interfaces as Access Pathways for Individuals with Severe Disabilities , 2013, Assistive technology : the official journal of RESNA.

[50]  A. Majnemer,et al.  Virtual reality as a therapeutic modality for children with cerebral palsy , 2010, Developmental neurorehabilitation.

[51]  B. Allison,et al.  The Asilomar Survey: Stakeholders’ Opinions on Ethical Issues Related to Brain-Computer Interfacing , 2011, Neuroethics.

[52]  C. G. Lim,et al.  A Brain-Computer Interface Based Attention Training Program for Treating Attention Deficit Hyperactivity Disorder , 2012, PloS one.

[53]  Rajesh P. N. Rao,et al.  High gamma mapping using EEG , 2010, NeuroImage.

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

[55]  E. Friedrich,et al.  Brain–computer interface game applications for combined neurofeedback and biofeedback treatment for children on the autism spectrum , 2014, Front. Neuroeng..

[56]  Brittany M. Young,et al.  Dose-response relationships using brain–computer interface technology impact stroke rehabilitation , 2015, Front. Hum. Neurosci..

[57]  H. Bekkering,et al.  Compromised motor planning and Motor Imagery in right Hemiparetic Cerebral Palsy. , 2010, Research in developmental disabilities.

[58]  J. Wolpaw,et al.  Brain–computer interfaces in neurological rehabilitation , 2008, The Lancet Neurology.

[59]  T. Ward,et al.  Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke. , 2015, Annals of physical and rehabilitation medicine.

[60]  Helge B. D. Sørensen,et al.  Brain-computer interface using P300 and virtual reality: A gaming approach for treating ADHD , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[61]  Jie Li,et al.  A Prior Neurophysiologic Knowledge Free Tensor-Based Scheme for Single Trial EEG Classification , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[62]  Wojciech Kułak,et al.  Neurophysiologic and neuroimaging studies of brain plasticity in children with spastic cerebral palsy , 2006, Experimental Neurology.

[63]  J. E. Korteling,et al.  Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls , 2015, Front. Neurosci..

[64]  Zachary V Freudenburg,et al.  Decoding Motor Signals From the Pediatric Cortex: Implications for Brain-Computer Interfaces in Children , 2011, Pediatrics.

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

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

[67]  Cuntai Guan,et al.  On the use of convolutional neural networks and augmented CSP features for multi-class motor imagery of EEG signals classification , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[68]  Gregg C. Vanderheiden,et al.  Mental workload during brain–computer interface training , 2012, Ergonomics.

[69]  Theresa M. Vaughan,et al.  A Novel Dry Electrode for Brain-Computer Interface , 2009, HCI.

[70]  Reinhold Scherer,et al.  A Co-Adaptive Brain-Computer Interface for End Users with Severe Motor Impairment , 2014, PloS one.

[71]  A. Pavlovic,et al.  Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. , 2016, Journal of neurophysiology.

[72]  Daniel Brandeis,et al.  Are treatment effects of neurofeedback training in children with ADHD related to the successful regulation of brain activity? A review on the learning of regulation of brain activity and a contribution to the discussion on specificity , 2015, Front. Hum. Neurosci..

[73]  Hubert Cecotti,et al.  Spelling with non-invasive Brain–Computer Interfaces – Current and future trends , 2011, Journal of Physiology-Paris.

[74]  Jaime Gómez Gil,et al.  Brain Computer Interfaces, a Review , 2012, Sensors.

[75]  Brendan Z. Allison,et al.  Comparison of Dry and Gel Based Electrodes for P300 Brain–Computer Interfaces , 2012, Front. Neurosci..