Wireless Brain-Robot Interface: User Perception and Performance Assessment of Spinal Cord Injury Patients

Patients suffering from life-changing disability due to Spinal Cord Injury (SCI) increasingly benefit from assistive robotics technology. The field of brain-computer interfaces (BCIs) has started to develop mature assistive applications for those patients. Nonetheless, noninvasive BCIs still lack accurate control of external devices along several degrees of freedom (DoFs). Unobtrusiveness, portability, and simplicity should not be sacrificed in favor of complex performance and user acceptance should be a key aim among future technological directions. In our study 10 subjects with SCI (one complete) and 10 healthy controls were recruited. In a single session they operated two anthropomorphic 8-DoF robotic arms via wireless commercial BCI, using kinesthetic motor imagery to perform 32 different upper extremity movements. Training skill and BCI control performance were analyzed with regard to demographics, neurological condition, independence, imagery capacity, psychometric evaluation, and user perception. Healthy controls, SCI subgroup with positive neurological outcome, and SCI subgroup with cervical injuries performed better in BCI control. User perception of the robot did not differ between SCI and healthy groups. SCI subgroup with negative outcome rated Anthropomorphism higher. Multi-DoF robotics control is possible by patients through commercial wireless BCI. Multiple sessions and tailored BCI algorithms are needed to improve performance.

[1]  J. Fermaglich Electric Fields of the Brain: The Neurophysics of EEG , 1982 .

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

[3]  David P. Doane,et al.  Measuring Skewness: A Forgotten Statistic? , 2011 .

[4]  Ming Yin,et al.  Listening to Brain Microcircuits for Interfacing With External World—Progress in Wireless Implantable Microelectronic Neuroengineering Devices , 2010, Proceedings of the IEEE.

[5]  Francois Routhier,et al.  Evaluation of the JACO robotic arm: Clinico-economic study for powered wheelchair users with upper-extremity disabilities , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[6]  Panagiotis D. Bamidis,et al.  Thessaloniki Active and Healthy Ageing Living Lab: the roadmap from a specific project to a living lab towards openness , 2016, PETRA.

[7]  Duncan Cramer,et al.  Fundamental Statistics for Social Research: Step-by-Step Calculations and Computer Techniques Using SPSS for Windows , 1998 .

[8]  C Grozea,et al.  On the feasibility of using motor imagery EEG-based brain–computer interface in chronic tetraplegics for assistive robotic arm control: a clinical test and long-term post-trial follow-up , 2012, Spinal Cord.

[9]  Stephanie M. Roldan,et al.  Object Recognition in Mental Representations: Directions for Exploring Diagnostic Features through Visual Mental Imagery , 2017, Front. Psychol..

[10]  D L Wolfe,et al.  Spasticity outcome measures in spinal cord injury: psychometric properties and clinical utility , 2008, Spinal Cord.

[11]  M. Reiner,et al.  Perspectives and possible applications of the rubber hand and virtual hand illusion in non-invasive rehabilitation: Technological improvements and their consequences , 2014, Neuroscience & Biobehavioral Reviews.

[12]  M. Grosse-Wentrup,et al.  Towards brain-robot interfaces in stroke rehabilitation , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[13]  Kerstin Dautenhahn,et al.  Methodology & Themes of Human-Robot Interaction: A Growing Research Field , 2007 .

[14]  Aleksandra Vuckovic,et al.  Interaction of BCI with the underlying neurological conditions in patients: pros and cons , 2014, Front. Neuroeng..

[15]  Tele Tan,et al.  3D visualization of movements can amplify motor cortex activation during subsequent motor imagery , 2015, Front. Hum. Neurosci..

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

[17]  William Petersen,et al.  Society and the Adolescent Self-Image. Morris Rosenberg. Princeton University Press, Princeton, N.J., 1965. xii + 326 pp. $6.50 , 1965 .

[18]  Richard M. Leahy,et al.  Electromagnetic brain mapping , 2001, IEEE Signal Process. Mag..

[19]  Bart Vanrumste,et al.  Journal of Neuroengineering and Rehabilitation Open Access Review on Solving the Inverse Problem in Eeg Source Analysis , 2022 .

[20]  Panagiotis Bamidis,et al.  Effects of imagery training on cognitive performance and use of physiological measures as an assessment tool of mental effort , 2007, Brain and Cognition.

[21]  Loris Pignolo,et al.  Robotics in neuro-rehabilitation. , 2009, Journal of rehabilitation medicine.

[22]  Panagiotis D. Bamidis,et al.  Development of MERCURY version 2.0 robotic arms for rehabilitation applications , 2015, PETRA.

[23]  Stephen T. Foldes,et al.  Neuroprosthetic technology for individuals with spinal cord injury , 2013, The journal of spinal cord medicine.

[24]  Cuntai Guan,et al.  A clinical study of motor imagery-based brain-computer interface for upper limb robotic rehabilitation , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  Panagiotis D. Bamidis,et al.  Density based clustering on indoor kinect location tracking: A new way to exploit active and healthy aging living lab datasets , 2015, 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE).

[26]  Bin He,et al.  Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks , 2016, Scientific Reports.

[27]  Panagiotis D Bamidis,et al.  Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms , 2017, BioMed research international.

[28]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[29]  Michael Hillman,et al.  Rehabilitation robotics from past to present - a historical perspective , 2003 .

[30]  P. Peckham,et al.  Functional electrical stimulation for neuromuscular applications. , 2005, Annual review of biomedical engineering.

[31]  I Gelernter,et al.  The Spinal Cord Independence Measure (SCIM) version III: Reliability and validity in a multi-center international study , 2007, Disability and rehabilitation.

[32]  Muhammad Abd-El-Barr,et al.  Long-term Training With a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients. , 2016, Neurosurgery.

[33]  Bin He,et al.  Classification of motor imagery by means of cortical current density estimation and Von Neumann entropy , 2007, Journal of neural engineering.

[34]  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.

[35]  E. Broadbent Interactions With Robots: The Truths We Reveal About Ourselves , 2017, Annual review of psychology.

[36]  Bin He,et al.  Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: An EEG and fMRI study of motor imagery and movements , 2010, NeuroImage.

[37]  N. Birbaumer,et al.  The Influence of Psychological State and Motivation on Brain–Computer Interface Performance in Patients with Amyotrophic Lateral Sclerosis – a Longitudinal Study , 2010, Front. Neuropharma..

[38]  Dana Kulic,et al.  Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots , 2009, Int. J. Soc. Robotics.

[39]  R.R. Harrison,et al.  Wireless Neural Recording With Single Low-Power Integrated Circuit , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[40]  Nitish V. Thakor,et al.  Brain-machine interface facilitated neurorehabilitation via spinal stimulation after spinal cord injury: Recent progress and future perspectives , 2016, Brain Research.

[41]  A. Beck,et al.  Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation , 1988 .

[42]  Jonas B. Zimmermann,et al.  Neural interfaces for the brain and spinal cord—restoring motor function , 2012, Nature Reviews Neurology.

[43]  Bin He,et al.  EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks , 2016, IEEE Transactions on Biomedical Engineering.

[44]  Panagiotis Bamidis,et al.  The effects of a computer-based cognitive and physical training program in a healthy and mildly cognitive impaired aging sample , 2014, Aging & mental health.

[45]  K. Müller,et al.  Psychological predictors of SMR-BCI performance , 2012, Biological Psychology.

[46]  Panagiotis D. Bamidis,et al.  Commercial BCI Control and Functional Brain Networks in Spinal Cord Injury: A Proof-of-Concept , 2017, 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS).

[47]  Gerhard Schmeisser,et al.  EVALUATION OF THE APL/JHU ROBOT ARM WORK STATION. , 1986 .

[48]  Martijn P. F. Berger,et al.  Exploring Task- and Student-Related Factors in the Method of Propositional Manipulation (MPM) , 2011 .

[49]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[50]  Kyle D. Fitle,et al.  Robot-Assisted Training of Arm and Hand Movement Shows Functional Improvements for Incomplete Cervical Spinal Cord Injury , 2017, American journal of physical medicine & rehabilitation.

[51]  V Kaiser,et al.  Motor imagery-induced EEG patterns in individuals with spinal cord injury and their impact on brain–computer interface accuracy , 2014, Journal of neural engineering.

[52]  A. Schwartz,et al.  High-performance neuroprosthetic control by an individual with tetraplegia , 2013, The Lancet.

[53]  Panagiotis D. Bamidis,et al.  A lightweight framework for transparent cross platform communication of controller data in ambient assisted living environments , 2015, Inf. Sci..

[54]  Woo-Keun Yoon,et al.  User evaluation to apply the robotic arm RAPUDA for an upper-limb disabilities Patient's Daily Life , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[55]  Andrei Krassioukov,et al.  International standards for neurological classification of spinal cord injury, revised 2011. , 2012, Topics in spinal cord injury rehabilitation.

[56]  Luis Montesano,et al.  Control of an Ambulatory Exoskeleton with a Brain–Machine Interface for Spinal Cord Injury Gait Rehabilitation , 2016, Front. Neurosci..

[57]  Richard W. Bohannon,et al.  Interrater reliability of a modified Ashworth scale of muscle spasticity. , 1987, Physical therapy.

[58]  H. Kwee,et al.  First experimentation of the Spartacus telethesis in a clinical environment , 1983, Paraplegia.

[59]  Stuart D. Harshbarger,et al.  An Overview of the Developmental Process for the Modular Prosthetic Limb , 2011 .

[60]  Robert Tomšik Power Comparisons of Shapiro-Wilk, Kolmogorov-Smirnov and Jarque-Bera Tests , 2019 .

[61]  Sylvain Baillet,et al.  Magnetoencephalography for brain electrophysiology and imaging , 2017, Nature Neuroscience.

[62]  Niels Birbaumer,et al.  Brain–Computer Interface in Neurorehabilitation , 2009 .

[63]  Christina Darviri,et al.  Rosenberg Self-Esteem Scale Greek Validation on Student Sample , 2014 .

[64]  Panagiotis D. Bamidis,et al.  Visual Versus Kinesthetic Motor Imagery for BCI Control of Robotic Arms (Mercury 2.0) , 2017, 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS).