Опубликованы главы в книгах "Brain–Computer Interfaces Handbook" и "Brain–Computer Interface Research"!

Михаил Лебедев и Алексей Осадчий опубликовали главы в книгах "Brain–Computer Interfaces Handbook" 2020 года и "Brain–Computer Interface Research" 2018 года.

Глава под названием "Bidirectional neural interfaces", опубликованная в сборнике "Brain–Computer Interfaces Handbook: Technological and Theoretical Advances" 2018 года, доступна по ссылке. 
Аннотация:
Bidirectional neural interfaces link the nervous system to external devices, itself, or even the nervous systems of different individuals to treat a neural disorder, augment brain functions, or provide a means for entertainment. Such interfaces combine an efferent loop that handles information derived from neural activity and an afferent loop that delivers signals to the brain. For example, a sensorized neuroprosthetic limb can be controlled by the brain motor activity while sending signals from the prosthetic sensors back to the brain. In this chapter, we review the basic components needed for bidirectional interfaces and consider several implementations of such systems. Among the large number of relevant methodologies, we highlight electrocorticographic grids as an approach particularly suitable for developing practical interfaces for patients suffering from sensory and motor disabilities.

Глава под названием "Generating Handwriting from Multichannel Electromyographic Activity", опубликованная в сборнике "Brain–Computer Interface Research", доступна по ссылке.
Аннотация:
Handwriting is an advanced motor skill and one of the key developments in human culture. Here we show that handwriting can be decoded—offline and online—from electromyographic (EMG) signals recorded from multiple hand and forearm muscles. We convert EMGs into continuous handwriting traces and into discretely decoded font characters. For this purpose, we use Wiener and Kalman filters, and machine learning algorithms. Our approach is applicable to clinical neural prostheses for restoration of dexterous hand movements, and to medical diagnostics of neural disorders that affect handwriting. We also propose that handwriting could be decoded from cortical activity, such as the activity recorded with electrocorticography (ECoG).