350 rub
Journal Technologies of Living Systems №7 for 2016 г.
Article in number:
Information streams at formation of the additional channel of feedback for the brain-computer interface
Authors:
Ya.A. Тurovsky - Ph.D. (Med.), Associate Professor, Head of Laboratory, Department of Medical Cybernetics Digital, Voronezh State University, Voronezh
E-mail: yaroslav_turovsk@mail.ru
S.D. Kurgalin - Dr.Sc. (Phys.-Math.), Professor, Head of Digital, Voronezh State University, Voronezh
E-mail: kurgalin@bk.ru
A.A. Vakhtin - Ph.D. (Phys.-Math.), Associate Professor, Senior Programmer, Medical Cybernetics Laboratory, Department of Digital Technology; Associate Professor, Department of Programming and Information Technologies, Voronezh State University, Voronezh
E-mail: alvahtin@gmail.com
S.V. Borzunov - Ph.D. (Phys.-Math.), Associate Professor, Digital Technology,
Voronezh State University, Voronezh
E-mail: sborzunov@gmail.com
A.V. Alekseev - Master, Department of Digital Technology; Laboratory of Technician Medical Cybernetics, Voronezh State University, Voronezh
Abstract:
One of the promising ways to improve human-computer interaction is the creation of so-called brain-computer interface (NCI, synonym ? «brain-computer interface»), organically grown systems of biofeedback. To detect changes in brain activity requires a certain time, which includes the accumulation of the required amount of data that contains the desired activity, and the time for its processing. In the framework of classical control scheme brain-computer interface at the beginning of a conscious generation of the user on the managed device commands to the actual output of this command, the user has no idea what kind of team of a possible alphabet teams considered software-hardware complex in terms of the choice to send on the effector unit, the correlation between the probability of selection of teams, etc. As a result, the user can see what kind of team to implement an interface only when a device or program-effector executed this command. In this paper presents an approach to the formation of an additional channel of communication and feedback system, synchronous and asynchronous NKI. The result of the formation of an additional channel of communication interface, the user is to create a firmware additional information flow, providing user-operator real-time NKI (or with minimal time delay) information on the progress of his treatment patterns of brain activity. If the formation of an additional feedback channel it is important to emphasize that the information containing the preliminary results of the calculations for the selection commands for the device-effector, must be presented to the operator interface in an accessible form for him. This message is carried using a visual, audible or tactile communication channel, both separately and in any combination thereof. It was demonstrated that the formation of the channel for asynchronous NKI possible only with the use of algorithms that exhibit intermediate data signal processing. For most NKI synchronous such a decision is a \"discrete\" out of necessity due to the accumulation of brain potential to underpin the formation of this type of interface commands. The problem of resource intensity of this approach. It is shown that significant limitations on resource consumption, based on physiological characteristics of human perception, no. The proposed approach can be used to improve a wide class of brain-computer interfaces.
Pages: 34-40
References
- Wolpaw J.R., McFarland D.J. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans // Proc. Natl. Acad. Sci. USA. 2004. V. 101. № 51.
- Wolpaw J.R., Birbaumer N., McFarland D.J., Pfurtscheller G., Theresa M. Brain-computer interfaces for communication and control // Vaughan Clinical Neurophysiology. 2002. № 113. R. 767-791.
- Borzunov C.V., Kurgalin S.D., Maksimov A.V. i dr. Ocenka skorosti raboty nejjrokompjuternogo interfejjsa, osnovannogo na tekhnologii SSVEP // Izvestija RAN. Ser. Teorija i sistemy upravlenija. 2014. № 1. S. 121-129.
- Nicolas-Alonso L. Fernando, Gomez-Gil Jaime. Brain Computer Interfaces. Sensors. 2012. P. 1211-1279.
- Lotte F., Congedo M., L´ecuyer A., Lamarche F., Arnaldi B. A review of classification algorithms for EEG-based brain-computer interfaces // Journal of Neural Engineering. 2007. № 4. R. R1-R13. doi:10.1088/1741-2560/4/2/R01.
- Gao X., Xu D., Cheng M. et al. A BCI-Based Environmental Controller for the Motion- Disabled // IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2003. V. 11. № 2. P. 137-140.
- Turovskijj JA.A., Kurgalin S.D. Ocenka skorosti raboty nejjrokompjuternogo interfejjsa, realizovannogo s ispolzovaniem gibridnogo intellekta // Biomedicinskaja radioehlektronika. 2015. № 3. S. 61-70.
- Turovskijj JA.A., Kurgalin S.D. Modelirovanie nejjrokompjuternogo interfejjsa na osnove gibridnogo intellekta // Trudy XII Vseros. soveshhanija po problemam upravlenija «VSPU-2014». M.: IPU im. V.A. Trapeznikova RAN. 2014. S. 6422-6431.