The Efficiency of the Brain-Computer Interfaces Based on Motor Imagery with Tactile and Visual Feedback

In this study we compared tactile and visual feedbacks for the motor imagery-based brain–computer interface (BCI) in five healthy subjects. A vertical green bar from the center of the fixing cross to the edge of the screen was used as visual feedback. Vibration motors that were placed on the forearms of the right and the left hands and on the back of the subject’s neck were used as tactile feedback. A vibration signal was used to confirm the correct classification of the EEG patterns of the motor imagery of right and left hand movements and the rest task. The accuracy of recognition in the classification of the three states (right hand movement, left hand movement, and rest) in the BCI without feedback exceeded the random level (33% for the three states) for all the subjects and was rather high (67.8% ± 13.4% (mean ± standard deviation)). Including the visual and tactile feedback in the BCI did not significantly change the mean accuracy of recognition of mental states for all the subjects (70.5% ± 14.8% for the visual feedback and 65.9% ± 12.4% for the tactile feedback). The analysis of the dynamics of the movement imagery skill in BCI users with the tactile and visual feedback showed no significant differences between these types of feedback. Thus, it has been found that the tactile feedback can be used in the motor imagery-based BCI instead of the commonly used visual feedback, which greatly expands the possibilities of the practical application of the BCI.

[1]  Michael Tangermann,et al.  Listen, You are Writing! Speeding up Online Spelling with a Dynamic Auditory BCI , 2011, Front. Neurosci..

[2]  Shuichi Nishio,et al.  The Importance of Visual Feedback Design in BCIs; from Embodiment to Motor Imagery Learning , 2016, PloS one.

[3]  Jean-Claude Baron,et al.  Motor Imagery to Enhance Recovery After Subcortical Stroke: Who Might Benefit, Daily Dose, and Potential Effects , 2008, Neurorehabilitation and neural repair.

[4]  N. Birbaumer,et al.  An auditory oddball (P300) spelling system for brain-computer interfaces. , 2009, Psychophysiology.

[5]  Chi Thanh Vi,et al.  Continuous Tactile Feedback for Motor-Imagery Based Brain-Computer Interaction in a Multitasking Context , 2015, INTERACT.

[6]  José del R. Millán,et al.  Freeing the visual channel by exploiting vibrotactile BCI feedback , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[7]  Bert Otten,et al.  Motor imagery ability in stroke patients: the relationship between implicit and explicit motor imagery measures , 2013, Front. Hum. Neurosci..

[8]  I. N. Angulo-Sherman,et al.  Effect of different feedback modalities in the performance of brain-computer interfaces , 2014, 2014 International Conference on Electronics, Communications and Computers (CONIELECOMP).

[9]  Tobias Kaufmann,et al.  Comparison of tactile, auditory, and visual modality for brain-computer interface use: a case study with a patient in the locked-in state , 2013, Front. Neurosci..

[10]  H. Flor,et al.  A multimodal brain-based feedback and communication system , 2004, Experimental Brain Research.

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

[12]  Febo Cincotti,et al.  Vibrotactile Feedback for Brain-Computer Interface Operation , 2007, Computational Intelligence and Neuroscience.

[13]  Sofya Liburkina,et al.  Assessing motor imagery in brain-computer interface training: Psychological and neurophysiological correlates , 2017, Neuropsychologia.

[14]  Shoji Makino,et al.  Spatial Auditory Two-step Input Japanese Syllabary Brain-computer Interface Speller , 2014 .

[15]  E. Biryukova,et al.  Principles of motor recovery in post-stroke patients using hand exoskeleton controlled by the brain-computer interface based on motor imagery , 2017 .

[16]  Aleksandra Vuckovic,et al.  Using a motor imagery questionnaire to estimate the performance of a Brain–Computer Interface based on object oriented motor imagery , 2013, Clinical Neurophysiology.

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

[18]  N. Birbaumer,et al.  An auditory oddball brain–computer interface for binary choices , 2010, Clinical Neurophysiology.

[19]  José del R. Millán,et al.  Haptic Feedback Compared with Visual Feedback for BCI , 2006 .

[20]  Aaron P Batista,et al.  Comparing temporal aspects of visual, tactile, and microstimulation feedback for motor control , 2014, Journal of neural engineering.

[21]  E. Biryukova,et al.  Rehabilitation of Stroke Patients with a Bioengineered “Brain–Computer Interface with Exoskeleton” System , 2016, Neuroscience and Behavioral Physiology.

[22]  José del R. Millán,et al.  Quantification and reduction of visual load during BCI operation , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[23]  J. Peters,et al.  Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery , 2011, Journal of neural engineering.

[24]  Benjamin Blankertz,et al.  Control-display mapping in brain–computer interfaces , 2012, Ergonomics.

[25]  L. Cohen,et al.  Brain–computer interfaces: communication and restoration of movement in paralysis , 2007, The Journal of physiology.

[26]  B. Hjorth An on-line transformation of EEG scalp potentials into orthogonal source derivations. , 1975, Electroencephalography and clinical neurophysiology.

[27]  Sergi Bermúdez i Badia,et al.  NeuRow: An Immersive VR Environment for Motor-Imagery Training with the Use of Brain-Computer Interfaces and Vibrotactile Feedback , 2016, PhyCS.

[28]  D. McFarland,et al.  An auditory brain–computer interface (BCI) , 2008, Journal of Neuroscience Methods.

[29]  Philipp Birken,et al.  Numerical Linear Algebra , 2011, Encyclopedia of Parallel Computing.

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

[31]  Michael S. Lazar,et al.  Spatial patterns underlying population differences in the background EEG , 2005, Brain Topography.

[32]  Pavel Bobrov,et al.  Increased motor cortex excitability during motor imagery in brain-computer interface trained subjects , 2013, Front. Comput. Neurosci..

[33]  Thilo Hinterberger,et al.  An Auditory Brain-Computer Interface Based on the Self-Regulation of Slow Cortical Potentials , 2005, Neurorehabilitation and neural repair.

[34]  B. Blankertz,et al.  A New Auditory Multi-Class Brain-Computer Interface Paradigm: Spatial Hearing as an Informative Cue , 2010, PloS one.

[35]  T. Mulder Motor imagery and action observation: cognitive tools for rehabilitation , 2007, Journal of Neural Transmission.

[36]  Alexander Kaplan,et al.  Poor BCI Performers Still Could Benefit from Motor Imagery Training , 2016, HCI.