ORIGINAL RESEARCH

Development of a neurodevice with a biological feedback for compensating for lost motor functions

Bogdanov EA1, Petrov VA1, Botman SA1, Sapunov VV1, Stupin VA2, Silina EV3, Sinelnikova TG3, Patrushev MV1, Shusharina NN1
About authors

1 Institute of Chemistry and Biology,
Immanuel Kant Baltic Federal University, Kaliningrad, Russia

2 Department of Hospital Surgery No. 1, Faculty of General Medicine,
Pirogov Russian National Research Medical University, Moscow, Russia

3 Department of Human Pathology, Faculty of Postgraduate Professional Training of Physicians,
The First Sechenov Moscow State Medical University, Moscow

Correspondence should be addressed: Evgeny Bogdanov
ul. A. Nevskogo, d. 14, Kaliningrad, Russia, 236041; moc.liamg@vonadgobue

About paper

Funding: the work was supported by the Ministry of Education and Science of the Russian Federation (Grant Agreement no. RFMEFI57815X0140 dated October 27, 2015).

Acknowledgements: the authors thank Alexandr Romanov of the Rehabilitation Center of the Administrative Department of the President of the Russian Federation, Moscow; Raphael Oganov of State Research Center for Preventive Medicine, Moscow; Daniil Borchevkin, Alexey Belousov, Vladimir Savinov, Sergey Sokolov, and Alexey Medvedev for their scientific contribution and productive collaboration.

Received: 2016-03-31 Accepted: 2016-04-07 Published online: 2017-01-05
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Concurrent use of electrophysiological signals of various types, such as obtained from electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG), and others, increases the effectiveness of systems for external device control, namely, neural prostheses, exoskeletons, robotic wheelchairs and teleoperated robots. This article presents the results of the first tests of a multifunctional neurodevice capable of detecting EEG, EMG and EOG signals simultaneously (with EOG signals photoplethysmogram, SpO2 and temperature modules of the neurodevice were used). Measurement results were then compared to the data obtained from KARDi3 device (Medical Computer Systems, Russia) and Fluke 17b multimeter with a plug-in thermistor (Fluke Corporation, USA). The informative value and accuracy of both datasets were comparable. We also studied the effectiveness of EEG and EMG signal hybridization on the basis of the neurodevice of interest; it allowed for an increase of classification accuracy in all subjects by an average of 12.5 % up to the mean of 86.8 % (from 75 to 97 %).

Keywords: brain-computer interface, exoskeleton, electroencephalogram, neurodevice, electromyogram, electrooculogram, biological feedback

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