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Abstract

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.

Keywords

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.
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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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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