Neuroengineering of sensorimotor rhythm-based brain–computer interface (BCI ) systems is the process of using engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties of neural systems, engaged in the representation, planning, and execution of 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 motor system, an overview of recent findings in neuroimaging and electrophysiology studies of the motor cortical anatomy and networks, and the engineering approaches used to analyze motor cortical signals and translate them into control signals that computer programs and devices can interpret.

Specifically, the anatomy and physiology of the human motor system, focusing on the brain areas 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 extracellular recordings, and magnetoencephalography (MEG ) is integrated with coverage aimed at elucidating what is known about sensory motor oscillations and brain anatomy, which are used to generate control 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 dealing with 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 and entertainment, 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.


Fractional Anisotropy Motor Imagery Rate Code Brain Computer Interface Efference Copy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.





brain–computer interface


brain–machine interface


bereitschafts potential


cingulate motor area


central nervous system


covariate shift minimization


common spatial pattern


corticospinal tract


degree of freedom


data space adaptation


diffusion tensor imaging








event-related desynchronization


event-related synchronization


fractional anisotropy


functional magneto-resonance imaging


fuzzy neural network


genetic algorithm


gray matter


higher frequency band


independent component analysis




inferior parietal lobe


inter-spike interval




linear discriminant analysis


lower frequency band


local field potential


lateralized readiness potential


motor cortex




man–machine learning dilemma


movement time


movement-related cortical potentials


magnetic resonance imaging


medial temporal


neural network


negative slope


principal component analysis


probabilistic classifier vector machine


Parkinson disease


positron emission tomography


partial mutual information


pyramidal neuron


power spectral density


particle swarm optimization


peri-stimulus-time histogram


pursuit-tracking task


persistent vegetative state


recurrent neural network


readiness potential


reaction time


superior longitudinal fasciculus


supplementary motor area


structural magnetic resonance imaging


sensorimotor rhythm


self-organizing fuzzy neural network


serial reaction time


single unit


support vector machine


total experiment time




white matter


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Intelligent Systems Research CentreUniversity of UlsterDerry, Northern IrelandUK
  2. 2.Electrical, Electronics and Communication EngineeringHolon Institute of Technology (H.I.T.)HolonIsrael

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