Abstract
Neuroengineering of sensorimotor rhythm-based brain–computer interface (GlossaryTerm
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 (GlossaryTerm
fMRI
and GlossaryTermsMRI
), electrocorticography (GlossaryTermECoG
), electroencephalography (GlossaryTermEEG
), intracortical single unit activity (GlossaryTermSU
) and multiple unit extracellular recordings, and magnetoencephalography (GlossaryTermMEG
) 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 GlossaryTermBCI
. 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 GlossaryTerm
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.Access this chapter
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Abbreviations
- 2-D:
-
two-dimensional
- 3-D:
-
three-dimensional
- BCI:
-
brain–computer interface
- BMI:
-
brain–machine interface
- BP:
-
bereitschafts potential
- CMA:
-
cingulate motor area
- CNS:
-
central nervous system
- CSM:
-
covariate shift minimization
- CSP:
-
common spatial pattern
- CST:
-
corticospinal tract
- DOF:
-
degree of freedom
- DSA:
-
data space adaptation
- DTI:
-
diffusion tensor imaging
- ECoG:
-
electrocorticography
- EEG:
-
electroencephalography
- EMG:
-
electromyography
- ERD:
-
event-related desynchronization
- ERS:
-
event-related synchronization
- FA:
-
fractional anisotropy
- fMRI:
-
functional magneto-resonance imaging
- FNN:
-
fuzzy neural network
- GA:
-
genetic algorithm
- GM:
-
gray matter
- HFB:
-
higher frequency band
- ICA:
-
independent component analysis
- IN:
-
interneuron
- IPL:
-
inferior parietal lobe
- ISI:
-
inter-spike interval
- KL:
-
Kullback–Leibler
- LDA:
-
linear discriminant analysis
- LFB:
-
lower frequency band
- LFP:
-
local field potential
- LRP:
-
lateralized readiness potential
- M1:
-
motor cortex
- MEG:
-
magnetoencephalography
- MMLD:
-
man–machine learning dilemma
- MOT:
-
movement time
- MRCP:
-
movement-related cortical potentials
- MRI:
-
magnetic resonance imaging
- MT:
-
medial temporal
- NN:
-
neural network
- NS:
-
negative slope
- PCA:
-
principal component analysis
- PCVM:
-
probabilistic classifier vector machine
- PD:
-
Parkinson disease
- PET:
-
positron emission tomography
- PMI:
-
partial mutual information
- PN:
-
pyramidal neuron
- PSD:
-
power spectral density
- PSO:
-
particle swarm optimization
- PSTH:
-
peri-stimulus-time histogram
- PTT:
-
pursuit-tracking task
- PVS:
-
persistent vegetative state
- RNN:
-
recurrent neural network
- RP:
-
readiness potential
- RT:
-
reaction time
- SLF:
-
superior longitudinal fasciculus
- SMA:
-
supplementary motor area
- sMRI:
-
structural magnetic resonance imaging
- SMR:
-
sensorimotor rhythm
- SOFNN:
-
self-organizing fuzzy neural network
- SRT:
-
serial reaction time
- SU:
-
single unit
- SVM:
-
support vector machine
- TET:
-
total experiment time
- TN:
-
thalamus
- WM:
-
white matter
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Coyle, D., Sosnik, R. (2015). Neuroengineering. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_39
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