Robotic orthosis compared to virtual hand for Brain–Computer Interface feedback

Abstract Brain–Computer Interfaces (BCI) allow the control of external devices by decoding the users’ intentions from their central nervous system. Feedback, one of the main elements of a closed-loop BCI, is used to enhance the user's performance. The present work aimed to compare the effect of two different feedback sources; congruent anatomical visual hand representation and passive hand movement on BCI performance and cortical activations. Electroencephalography of 12 healthy right-handed subjects was recorded to set a BCI activated by right-hand motor imagery. Afterward, the subjects were asked to control the system by imagining the movement. The system provided either visual feedback, shown on a computer screen or kinesthetic feedback, provided by a robotic hand orthosis. Differences in performance and cortical activations were assessed, using classification accuracy and event-related desynchronization/synchronization in μ and β bands, respectively. Performance was significantly better with kinesthetic feedback as it allowed for higher correct classification of motor imagery. Cortical activations in the ipsilateral central channel in μ were different between the two feedback modalities. Our results imply that healthy subjects can achieve a greater degree of control using a motor imagery-based BCI with kinesthetic feedback than with anatomically congruent visual feedback. Furthermore, cortical activation differences show that kinesthetic feedback seems to elicit higher recruitment of sensorimotor cortex brain cells, which probably reflects enhanced local information modulation related to fine motor processing. Therefore, kinesthetic feedback provided by a robotic orthosis could be a more suitable feedback strategy for BCI systems designed for neuromodulation and neurorehabilitation.

[1]  Cuntai Guan,et al.  Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[2]  Aamir Saeed Malik,et al.  Discrimination of four class simple limb motor imagery movements for brain-computer interface , 2018, Biomed. Signal Process. Control..

[3]  J. Pernier,et al.  Oscillatory γ-Band (30–70 Hz) Activity Induced by a Visual Search Task in Humans , 1997, The Journal of Neuroscience.

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

[5]  Panagiotis D. Bamidis,et al.  Investigating the Role of Alpha and Beta Rhythms in Functional Motor Networks , 2016, Neuroscience.

[6]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[7]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[8]  Huafu Chen,et al.  Quantification of the power changes in BOLD signals using Welch spectrum method during different single-hand motor imageries. , 2014, Magnetic resonance imaging.

[9]  Edgar Erdfelder,et al.  G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences , 2007, Behavior research methods.

[10]  N. Thakor,et al.  Journal of Neuroengineering and Rehabilitation Open Access a Brain-computer Interface with Vibrotactile Biofeedback for Haptic Information , 2007 .

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

[12]  R. Oostenveld,et al.  Nonparametric statistical testing of EEG- and MEG-data , 2007, Journal of Neuroscience Methods.

[13]  Aamir Saeed Malik,et al.  Modulation of sensorimotor rhythms for brain-computer interface using motor imagery with online feedback , 2017, Signal, Image and Video Processing.

[14]  Fotis Liarokapis,et al.  EEG-based BCI and video games: a progress report , 2018, Virtual Reality.

[15]  Spatial–temporal dynamics of gesture–speech integration: a simultaneous EEG-fMRI study , 2018, Brain Structure and Function.

[16]  Alireza Gharabaghi,et al.  Bridging the gap between motor imagery and motor execution with a brain–robot interface , 2015, NeuroImage.

[17]  Jonathan R Wolpaw,et al.  Brain–computer interface systems: progress and prospects , 2007, Expert review of medical devices.

[18]  Jessica Cantillo-Negrete,et al.  Motor Imagery-Based Brain-Computer Interface Coupled to a Robotic Hand Orthosis Aimed for Neurorehabilitation of Stroke Patients , 2018, Journal of healthcare engineering.

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

[20]  Gordon H Guyatt,et al.  Design, analysis, and presentation of crossover trials , 2009, Trials.

[21]  Clemens Brunner,et al.  Better than random? A closer look on BCI results , 2008 .

[22]  Shigeo Tanabe,et al.  Combined effect of motor imagery and peripheral nerve electrical stimulation on the motor cortex , 2013, Experimental Brain Research.

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

[24]  Toshiyuki Kondo,et al.  Effect of instructive visual stimuli on neurofeedback training for motor imagery-based brain-computer interface. , 2015, Human movement science.

[25]  Febo Cincotti,et al.  Vibrotactile Feedback for Brain-Computer Interface Operation , 2007, Comput. Intell. Neurosci..

[26]  Hasan Ayaz,et al.  Virtual and Actual Humanoid Robot Control with Four-Class Motor-Imagery-Based Optical Brain-Computer Interface , 2017, BioMed research international.

[27]  Jeng-Shyang Pan,et al.  Development of a Wearable Motor-Imagery-Based Brain–Computer Interface , 2016, Journal of Medical Systems.

[28]  Cuntai Guan,et al.  Facilitating motor imagery-based brain–computer interface for stroke patients using passive movement , 2016, Neural Computing and Applications.

[29]  Maria José Blanca-Mena,et al.  Brain-Computer Interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair , 2017, Journal of NeuroEngineering and Rehabilitation.

[30]  Alireza Gharabaghi,et al.  Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality , 2015, NeuroImage.

[31]  Nicola Smania,et al.  Modulation of event-related desynchronization in robot-assisted hand performance: brain oscillatory changes in active, passive and imagined movements , 2013, Journal of NeuroEngineering and Rehabilitation.

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

[33]  José del R. Millán,et al.  An Introduction to Brain-Computer Interfacing , 2007 .

[34]  M. Carrillo-de-la-Peña,et al.  Equivalent is not equal: Primary motor cortex (MI) activation during motor imagery and execution of sequential movements , 2008, Brain Research.

[35]  David J. Reinkensmeyer,et al.  Performance Assessment of a Brain–Computer Interface Driven Hand Orthosis , 2014, Annals of Biomedical Engineering.

[36]  Timothy Bardouille,et al.  The impact of goal-oriented task design on neurofeedback learning for brain–computer interface control , 2017, Medical & Biological Engineering & Computing.

[37]  Jessica Cantillo-Negrete,et al.  Mechanical structure prototype and control unit for an active orthosis for a human had , 2014, 2014 Pan American Health Care Exchanges (PAHCE).

[38]  Sarah N. Kraeutner,et al.  Motor imagery-based brain activity parallels that of motor execution: Evidence from magnetic source imaging of cortical oscillations , 2014, Brain Research.

[39]  L. Cohen,et al.  Brain–machine interfaces in neurorehabilitation of stroke , 2015, Neurobiology of Disease.

[40]  Christophe Morin,et al.  Neuromarketing: The New Science of Consumer Behavior , 2011 .

[41]  T. Sollfrank,et al.  The effect of multimodal and enriched feedback on SMR-BCI performance , 2016, Clinical Neurophysiology.

[42]  Andrzej Majkowski,et al.  The Impact of Different Visual Feedbacks in User Training on Motor Imagery Control in BCI , 2017, Applied Psychophysiology and Biofeedback.

[43]  Akio Kimura,et al.  Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke , 2014, Front. Neuroeng..

[44]  Kyuwan Choi,et al.  Electroencephalography (EEG)-based neurofeedback training for brain–computer interface (BCI) , 2013, Experimental Brain Research.

[45]  M. Franceschini,et al.  Action observation and mirror neuron network: a tool for motor stroke rehabilitation. , 2012, European journal of physical and rehabilitation medicine.

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