Nanobiotechnology for health-care

States Recognition for Brain-Computer Microelectrodes Interfaces.
Tchaikivsky P. I., Netreba A. V., Radchenko S. P.
Medical Radiophysics Department, Faculty of Radio Physics, Electronics and Computer Systems, Taras Shevchenko National University of Kyiv, Glushkova ave., 4g, Kyiv-03191, Ukraine.
E-mail: sprad@knu.ua

Brain-computer interface (BCI) is used to make intercommunication between humans and external devices. BCI has to read human's brain activity and transform it to computer commands or machine instructions.  These days, the facilities of the electroencephalography (EEG) are widely used for registration of initial signals in the brain-computer interfaces. Such application contains the brain bio-potentials measurement, signals recording and data processing. Simultaneous solution of these components will allow successful wide implementation of brain-computer interface. The advancement of nanotechnology allows fabrication of new types of nano-electrodes based on new micro materials [1] and takes away electrode limitation. Therefore in the present paper, issues of identity of EEG signals and the possibility of brain states recognition to transform brain activity into computer commands are discussed. Suggested phase portraits for the brain activity states are constructed based on the empirical mode decomposition (EMD) of the brain electrical activity signals. The results demonstrate interrelation of the traditional amplitude-frequency features of EEG signals and the shapes and sizes of elements of phase portraits obtained in different states. Results of EMD of EEG signals contain sinusoidal components with modulated amplitude and frequency which representation in phase coordinates conform to changes in the initial brain state. For example the data processing of sixteen subjects with closed eyes and open eyes shows that the difference between the two states is statistically significant according to Wilcoxon rank test. The combination of nano material electrodes' EEG and proposed algorithm for phase coordinates' representation offers new opportunities to develop biomedical devices and new robust communication links to produce BCI.

1.	Sen Lin, et al., A Flexible, Robust, and Gel-Free Electroencephalogram Electrode for Noninvasive Brain-Computer Interfaces // Nano Lett. 2019, 19, 10, pp. 6853–6861.
2.	R. Kovtun, et al., Exploiting EEG Signals for Eye Motion Tracking. // 2020 IEEE East-West Design & Test Symposium (EWDTS), Varna, Bulgaria, 2020 pp. 1-5. doi: 10.1109/EWDTS50664.2020.9225144