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Section BRAIN-COMPUTER INTERFACES, COGNITIVE NAVIGATION WORKSHOP AND NEUROENGINEERING
The human brain is likely to be the most convoluted and enigmatic object for the comprehensive studies. Due to their outstanding importance and the extraordinary complexity, the investigations of the brain require the active interdisciplinary cooperation of the scientists belonging to the different branches of science. In the present work we study cognitive brain activity in visual perception of ambiguous images being just one, but a very exciting task among an enormous number of open problems of brain researches. We propose the theoretical approach associated with the experimental technique to quantitatively characterize cognitive brain activity in perception of ambiguous images. The internal noise seems to plays an important role in the visual perception of such images. Based on the developed theoretical background and the obtained experimental data, we introduce the concept of effective noise intensity characterizing cognitive brain activity and propose the experimental technique for its measurement. The developed theory, using the methods of statistical physics, provides the solid experimentally approved basis for further understanding of brain functionality. Our theoretical and experimental findings are in excellent agreement with each other. The rather simple way to quantitatively characterize brain activity connected with the perception of ambiguous images may be a powerful tool to be used, e.g., in neurotechnology, as a braincomputer interface task, and in medicine for diagnostic and prognostic purposes. We expect that our work will be interesting and useful for scientists carrying out interdisciplinary research at the cutting edge of physics, neurophysiology and medicine.
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Modern Trends in Brain-Machine Interfaces
Mikhail A Lebedev*
Department of Neurobiology, Duke University, Durham, North Carolina 27710, USA. * Presenting e-mail: mikhail.a.lebedev@gmail.com
Considerable advances in brain-machine interface (BMI) technologies bring medicine closer to solving such challenges as treatment of paralysis and sensory disabilities caused by neural trauma and diseases. Toward this goal, there is an ongoing research on prosthetic limbs controlled by brain signals, and neural stimulation systems that restore sensations by stimulating sensory brain areas. Not so long ago depictions of such BMIs could be found only in science fiction. Nowadays, even the most futuristic ideas are becoming real. Several recent studies have demonstrated direct functional connections between the brain and robotic arms. Significant achievements have been made in the systems that restore hearing, vision, vestibular function and tactile sensations to people who suffer from sensory loss.
In addition to medical applications, BMIs are being developed for augmentation of brain function in normal humans. Examples include BMIs for computer gaming, and neurofeedback systems that detect drowsiness in long-distance drivers. In the future, BMI-based technologies will lead to new means of communication and hybrid systems that merge the nervous systems with artificial neural nets.
BMIs are an interdisciplinary field that involves neurophysiologists, neurosurgeons, neurologists, robotic and electrical engineers, mathematicians and programmers. Facilitated by these collaborative efforts, BMI filed is developing very rapidly, with the number of publications growing exponentially. Current BMIs can be classified into:
1. Motor BMIs. These systems record neural signals in the brain motor areas and transform them into control commands to external devices. Aided with motor BMIs, paralyzed patients can control prostheses of the arms and legs, and motorized wheelchairs.
2. Sensory BMIs. These are systems for restoration of vision, hearing, tactile sensations, proprioception and vestibular functions. In a typical design, sensory information is collected by an artificial sensor and transmitted to the brain using electrical stimulation of the brain sensory areas.
3. Cognitive BMIs. These devices decode higher-order brain signals, such as neural representation of decision making, emotions, and even thoughts.
A large number of methods have been developed for sampling neural signals and utilizing them in BMIs. These methods can be subdivided into two major classes: invasive and noninvasive recordings.
Noninvasive BMIs are safe to use and easy to implement. These include systems based on electroencephalography (EEG), magnetoencephalography (MEG), near-infrared spectrometry (NIRS), and functional magnetic resonance imaging (fMRI). Notwithstanding a number of advantages of these methods, their generally have low information transfer rate and are susceptible to artifacts. Additionally, noninvasive BMIs often require a considerable degree of concentration from the user, leading to fatigue.
Invasive BMIs utilize brain implants placed by a neurosurgeon on the brain surface or inserted into the brain tissue. With these methods, activity of single neurons and their populations can be recorded, which enables high-quality decoding. Modern invasive BMIs incorporate multiple recording channels. Invasive BMIs are also used to stimulate nervous tissue. Several clinical trials of invasive BMIs in humans have been already conducted.
Section BRAIN-COMPUTER INTERFACES, COGNITIVE NAVIGATION WORKSHOP AND NEUROENGINEERING
The future steps in this field include:
• Development of clinically relevant, bidirectional, multichannel BMIs that both decode neural activity and deliver sensory information to the brain.
• Development of advanced robotic prostheses capable of restoring mobility of paralyzed limb. These include exoskele-tons, prosthetic limbs and functional electrical stimulators that activate subjects' own muscles.
• Research on mathematical algorithms for decoding of brain activity. It is expected that this work will generate both efficient decoders for BMIs and new theories of brain processing.
• Development of multidisciplinary collaborations. Overall, BMIs are definitely the technology of the future.
Device for Electrophysiological Signal Recognition and Data Transmission on Wheelchairs
M. V. Patrushev*, E. A. Bogdanov and N.N. Shusharina
Institute of Chemistry and Biology Immanuel Kant Baltic Federal University, Kaliningrad, Russia. * Presenting e-mail: maxpatrushev@gmail.com
Aims
In this work we present the results of the first tests of a device capable for detecting EMG signals recognition and data transmission on wheelchairs.
Methods
In obtained results we have used electrophysiological signal, such as obtained from electromyogram (EMG), increases the effectiveness of systems for external device control: wheelchairs.
Results
It's obvious that development of a high-accuracy device that allows for continuous recording of physiological signals and transmits data to the external device (wheelchair) can yield very inspiring results. We have carried out a truly multidisciplinary study, at the first stage of which a prototype model of such device was created and tested. It was demonstrated that the signals obtained with our device were identical to those obtained with reliable analytical tools.
Conclusions
The results of EMG experiment showed the considerable advantage over joystick control. Due to the increased classification accuracy and flexibility, a device for EMG wheelchair control is more reliable and exhibits the new opportunities and freedom level for people with disabilities. The obtained results lead us to conclude that EMG recording can be used as alternative method for wheelchair control. We assume that improvements to the system and simultaneous use of various physiological signals will significantly help people with disabilities in a wheelchairs control.
Acknowledgements
The work was supported by the Ministry of Education and Science of the Russian Federation within the framework of the Federal Targeted Program for Research and Development in Priority Areas of Advancement of the Russian Scientific and Technological Complex for 2014-2020 (Grant Agreement no. RFMEFI57815X0140 dated October 27, 2015).
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