High classification accuracy of a motor imagery based brain-computer interface for stroke rehabilitation training

Now if we we just get them to write up a protocol on this and distribute it to all the stroke doctors and hospitals in the world. I agree that that is a total fantasy. 

High classification accuracy of a motor imagery based brain-computer interface for stroke rehabilitation training

Danut C. Irimia^{1, 2*}, Rupert Ortner³, Marian S. Poboroniuc², Bogdan E. Ignat⁴ and Christoph Guger^{1, 3*}

• ¹Guger Technologies (Austria), Austria
• ²Department of Energy Utilization, Electrical Drives and Industrial Automation, Gheorghe Asachi Technical University of Iași, Romania
• ³g.tec medical engineering GmbH, Austria
• ⁴Grigore T. Popa University of Medicine and Pharmacy, Romania

Motor imagery (MI) based brain-computer interfaces (BCI) extract commands in real-time and can be used to control a cursor, a robot or functional electrical stimulation (FES) devices. The control of FES devices is especially interesting for stroke rehabilitation, when a patient can use motor imagery to stimulate specific muscles in real-time. However, damage to motor areas resulting from stroke or other causes might impair control of a motor imagery BCI for rehabilitation.

The current work presents a comparative evaluation of the MI-based BCI control accuracy between stroke patients and healthy subjects. Five patients who had a stroke that affected the motor system participated in the current study, and were trained across 10-24 sessions lasting about one hour each with the recoveriX system. The participants’ EEG data were classified while they imagined left or right hand movements, and real-time feedback was provided on a monitor. If the correct imagination was detected, the FES was also activated to move the left or right hand. The grand average mean accuracy was 87.4% for all patients and sessions. All patients were able to achieve at least one session with a maximum accuracy above 96%. Both the mean accuracy and the maximum accuracy were surprisingly high and above results seen with healthy controls in prior studies.

Importantly, the study showed that stroke patients can control a MI BCI system with high accuracy relative to healthy persons. This may occur because these patients are highly motivated to participate in a study to improve their motor functions. Participants often reported early in the training of motor improvements and this caused additional motivation. However, it also reflects the efficacy of combining motor imagination, seeing continuous bar feedback, and real hand movement that also activates the tactile and proprioceptive systems. Results also suggested that motor function could improve even if classification accuracy did not, and suggest other new questions to explore in future work. Future studies will also be done with a first-person view 3D avatar to provide improved feedback and thereby increase each patients’ sense of engagement.

Keywords: Brain-Computer Interface1, motor imagery2, stroke3, rehabilitation4, classification accuracy5.

Received: 13 Jun 2017; Accepted: 08 Nov 2018.

Edited by:
Massimo Bergamasco, Scuola Sant'Anna di Studi Avanzati, Italy

Reviewed by:
Jeanine Stefanucci, University of Utah, United States
Pawel A. Herman, Royal Institute of Technology, Sweden  

Copyright: © 2018 Irimia, Ortner, Poboroniuc, Ignat and Guger. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
PhD. Danut C. Irimia, Guger Technologies (Austria), Graz, Austria, irimia@gtec.at
Dr. Christoph Guger, Guger Technologies (Austria), Graz, Austria, guger@gtec.at