Chapters in "Brain-Computer Interfaces Handbook" and "Brain-Computer Interface Research" are published!

Mikhail Lebedev and Alexey Ossadtchi have published chapters in the 2020 "Brain – Computer Interfaces Handbook" and 2018 "Brain – Computer Interface Research".

A chapter titled "Bidirectional neural interfaces" published in the 2018 Brain – Computer Interfaces Handbook: Technological and Theoretical Advances is available on the website.
Abstract:
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.

A chapter titled "Generating Handwriting from Multichannel Electromyographic Activity", published in "Brain-Computer Interface Research" is available on the website.
Annotation:
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).