Investigation of Characteristics of a Motor-Imagery Brain–Computer Interface with Quick-Response Tactile Feedback

One of the approaches in rehabilitation after a stroke is mental training by representation of movement using a brain-computer interface (BCI), which allows one to control the result of every attempt of imaginary movement. BCI technology is based on online analysis of an electroencephalogram (EEG), detecting moments of imaginary movement representation (reaction of sensorimotor rhythm desynchronization) and presenting these events in the form of changing scenes on a computer screen or triggering electro-mechanical devices, which essentially is feedback. Traditionally used visual feedback is not always optimal for poststroke patients. Earlier, the effectiveness of tactile feedback, triggered only after a long-time mental representation of the movement, for several seconds or more, was studied. In this work, the efficiency of quick tactile feedback with motor-imagery-based BCI was investigated during classification of short (0.5 s) EEG segments. It was shown that quick tactile feedback is not inferior to the visual feedback and that it is possible to create BCI with tactile feedback that allows a quick reward of physiologically effective attempts of motor imagery and operates with acceptable accuracy for practical use. Furthermore, under certain conditions, tactile feedback can lead to a greater degree of sensorimotor rhythm desynchronization in subjects in comparison with the visual feedback, which can serve as a basis for constructing an effective neurointerface training system.

[1]  Mihoko Niitsuma,et al.  Perception of tactile sensation using vibrotactile glove interface , 2012, 2012 IEEE 3rd International Conference on Cognitive Infocommunications (CogInfoCom).

[2]  M. V. Lukoyanov,et al.  Exoskeleton Control System Based on Motor-Imaginary Brain–Computer Interface , 2017 .

[3]  D X Cifu,et al.  Factors affecting functional outcome after stroke: a critical review of rehabilitation interventions. , 1999, Archives of physical medicine and rehabilitation.

[4]  Jan B. F. van Erp,et al.  A Tactile P300 Brain-Computer Interface , 2010, Front. Neurosci..

[5]  Marieke E. Thurlings,et al.  Controlling a Tactile ERP–BCI in a Dual Task , 2013, IEEE Transactions on Computational Intelligence and AI in Games.

[6]  O. G. Pavlova,et al.  Preliminary results of a controlled study of BCI–exoskeleton technology efficacy in patients with poststroke arm paresis , 2016 .

[7]  Minkyu Ahn,et al.  Journal of Neuroscience Methods , 2015 .

[8]  Peter Desain,et al.  Introducing the tactile speller: an ERP-based brain–computer interface for communication , 2012, Journal of neural engineering.

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

[10]  Xinjun Sheng,et al.  Long-term paired sensory stimulation training for improved motor imagery BCI performance via pavlovian conditioning theory , 2015, 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER).

[11]  A. K. Platonov,et al.  Principles of neurorehabilitation based on the brain-computer interface and biologically adequate control of the exoskeleton , 2013, Human Physiology.

[12]  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.

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

[14]  L. Connell,et al.  Somatosensory impairment after stroke: frequency of different deficits and their recovery , 2008, Clinical rehabilitation.

[15]  Shoji Makino,et al.  P300 responses classification improvement in tactile BCI with touch-sense glove , 2014, Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific.

[16]  Anne-Marie Brouwer,et al.  A tactile P 300 brain-computer interface , 2010 .

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

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

[19]  Antonio Frisoli,et al.  Effects of Continuous Kinaesthetic Feedback Based on Tendon Vibration on Motor Imagery BCI Performance , 2018, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[20]  A. Ya. Kaplan,et al.  Neurophysiological foundations and practical realizations of the brain–machine interfaces in the technology in neurological rehabilitation , 2016, Human Physiology.

[21]  M. V. Lukoyanov,et al.  The Efficiency of the Brain-Computer Interfaces Based on Motor Imagery with Tactile and Visual Feedback , 2018, Human Physiology.

[22]  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.