Details

Autor(en) / Beteiligte

• Volkova, K. V.
• Dagaev, N. I.
• Kiselev, A. S.
• Kasumov, V. R.
• Aleksandrov, M. V.
• Osadchiy, A. E.

Titel

The Brain–Computer Interface: Experience of Construction, Use, and Potential Routes to Improving Performance

Ist Teil von

• Neuroscience and behavioral physiology, 2018-11, Vol.48 (9), p.1128-1139

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2018

Quelle

Springer journals

Beschreibungen/Notizen

• Neurocomputer interfaces or, as they have come to be known in the Russian literature, brain–computer interfaces (BCI), are used in several areas and have the potential for uses in solving both research and applied tasks. Pilot studies in the clinical application of BCI to poststroke neurorehabilitation are currently under way [Frolov et al., 2013; Ang et al., 2010], and there are prospects for the use of BCI for direct restoration of movement/communication capabilities by creating an alternative information exchange channel with intelligent prostheses and the surroundings. Studies using electrophysiological data generate the need to process multidimensional, nonstationary signals, refl ecting complex physiological processes. Interfaces based on noninvasive technologies for recording brain activity do not as yet provide reliable information links with the user’s brain. The results of our studies show that improvements in the working characteristics of these systems can be obtained by constructing new machine learning algorithms considering the physiological and psychoemotional characteristics of BCI use. These algorithms can be developed either in the classical Bayesian paradigm or using state-of-the-art deep learning techniques. In addition, the creation of methods for the physiological interpretation of nonlinear decision rules found by multilayered structures opens up new potentials for the automatic and objective extraction of knowledge from experimental neurophysiological data. Despite the attractiveness of noninvasive technologies, radical increases in the throughput of BCI communication channels and the use of this technology to control prostheses can only be obtained using invasive methods of recording brain activity. Electrocorticograms (ECoG) are the least invasive of these technologies, and in the concluding part of this work we will demonstrate that ECoG can be used for decoding of the kinematic characteristics of finger movements.