Neuroengineering

Neuroengineering of sensorimotor rhythm-based brain–computer interface (BCI) systems is theprocess of using engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties of neural systems, engaged in the representation, planning, and executionof volitional movements, for the restoration and augmentation of human function via direct interactions between the nervous system and devices.This chapter reviews information that is fundamental for the complete and comprehensive understanding of this complex interdisciplinary research field, namely an overview of the motorsystem, an overview of recent findings in neuroimaging and electrophysiology studies of the motor cortical anatomy and networks, and the engineering approaches used to analyze motorcortical signals and translate them into control signals that computer programs and devices caninterpret. Specifically, the anatomy and physiology of the human motor system, focusing on the brainareas and spinal elements involved in the generation of volitional movements is reviewed. The stage is then set for introducing human prototypical motion attributes, sensorimotor learning, and several computational models suggested to explain psychophysical motor phenomena based on the current knowledge in the field of neurophysiology. An introduction to invasive and non-invasive neural recording techniques, including functional and structural magnetic resonance imaging (fMRI and sMRI), electrocorticography (ECoG), electroencephalography (EEG), intracortical single unit activity (SU) and multiple unit extracellularrecordings, and magnetoencephalography (MEG) is integrated with coverage aimed at elucidating what is known about sensory motor oscillations and brain anatomy, which are used to generatecontrol signals for brain actuated devices and alternative communication in BCI. Emphasis is on latest findings in these topics and on highlighting what information is accessible at each of the different scales and the levels of activity that are discernible or utilizable for the effective control of devices using intentional activation sensorimotor neurons and/or modulation of sensorimotor rhythms and oscillations.The nature, advantages, and drawbacks of various approaches and their suggested functions as the neural correlates of various spatiotemporal motion attributes are reviewed. Sections dealingwith signal analysis techniques, translation algorithms, and adaption to the brain’s non-stationary dynamics present the reader with a wide-ranging review of the mathematical and statistical techniques commonly used to extract and classify the bulk of neural information recorded by the various recording techniques and the challenges that are posed for deploying BCI systems for their intended uses, be it alternative communication and control, assistive technologies, neurorehabilitation, neurorestoration or replacement, or recreation andentertainment, among other applications. Lastly, a discussion is presented on the future of the field,highlighting newly emerging research directions and their potential ability to enhance our understanding of the human brain and specifically the human motor system and ultimately how that knowledge may lead to more advanced and intelligent computational systems.