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Neuroengineering

  • Chapter
Springer Handbook of Computational Intelligence

Part of the book series: Springer Handbooks ((SHB))

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 GlossaryTerm

sMRI

), electrocorticography (GlossaryTerm

ECoG

), electroencephalography (GlossaryTerm

EEG

), intracortical single unit activity (GlossaryTerm

SU

) and multiple unit extracellular recordings, and magnetoencephalography (GlossaryTerm

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 GlossaryTerm

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 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.

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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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Authors and Affiliations

  1. Intelligent Systems Research Centre, University of Ulster, Northland Rd, BT48 7JL, Derry, Northern Ireland, UK

    Damien Coyle

  2. Electrical, Electronics and Communication Engineering, Holon Institute of Technology (H.I.T.), 52 Golomb St., 5810201, Holon, Israel

    Ronen Sosnik

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    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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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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