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EEG-Based Machine Learning: Theory and Applications

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Handbook of Neuroengineering

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Abstract

Electroencephalography is a widely used clinical and research method to record and monitor the brain’s electrical activity – the electroencephalogram (EEG). Machine learning algorithms have been developed to extract information from the EEG to help in the diagnosis of several disorders (e.g., epilepsy, Alzheimer’s disease, and schizophrenia) and to identify various brain states. Despite the elegant and generally easy-to-use nature of machine learning algorithms in neuroscience, they can produce inaccurate and even false results when implemented incorrectly. In this chapter, we outline the general methodology for EEG-based machine learning, pattern recognition, and classification. First, a description of feature extraction from various domains is presented. This is followed by an overview of supervised and unsupervised feature-reduction methods. We then focus on classification algorithms, performance evaluation, and methods to prevent overfitting. Finally, we discuss two applications of EEG-based machine learning: brain-computer interface (BCI) and detection and prediction of microsleeps.

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Abbreviations

AdaBoost:

adaptive boosting

AUC-PR:

area under the curve of the precision recall

AUC-ROC:

area under the curve of the receiver operating characteristic

bagging:

bootstrap aggregating

BCI:

brain-computer interface

CSP:

common spatial pattern

EEG:

electroencephalogram

FBCSP:

filter bank common spatial pattern

FFT:

fast Fourier transform

fMRI:

functional magnetic resonance imaging

FN:

false negative

fNIRS:

functional near-infrared spectroscopy

FP:

false positive

FSULR:

unsupervised learning with ranking based feature selection

GM:

geometric mean

HA:

Hjorth activity

HC:

Hjorth complexity

HM:

Hjorth mobility

ICA:

independent component analysis

IWBW:

intensity-weighted bandwidth

IWMF:

intensity-weighted mean frequency

kNN:

k-nearest neighbour

KPCA:

kernel principal component analysis

LDA:

linear discriminant analysis

LOSO:

leave one-subject out

PCA:

principal component analysis

PPCA:

probabilistic principal component analysis

PR:

precision recall

Pr:

precision

ROC:

receiver operating characteristic

SN:

sensitivity

SP:

specificity

SSVEP:

steady-state visual evoked potential

SVM:

support vector machine

t-SNE:

t-distributed stochastic neighbour embedding

TN:

true negative

TP:

true positive

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Canterbury, Christchurch, New Zealand

    Reza Shoorangiz

  2. Department of Medicine, University of Otago, Christchurch, New Zealand

    Reza Shoorangiz

  3. Neurotechnology Research Programme, Christchurch, New Zealand

    Reza Shoorangiz

  4. New Zealand Brain Research Institute, Christchurch, New Zealand

    Reza Shoorangiz

  5. Department of Electrical and Computer Engineering, University of Canterbury, Christchurch, New Zealand

    Stephen J. Weddell

  6. Neurotechnology Research Programme, Christchurch, New Zealand

    Stephen J. Weddell

  7. Department of Electrical and Computer Engineering, University of Canterbury, Christchurch, New Zealand

    Richard D. Jones

  8. Department of Medicine, University of Otago, Christchurch, New Zealand

    Richard D. Jones

  9. Neurotechnology Research Programme, Christchurch, New Zealand

    Richard D. Jones

  10. New Zealand Brain Research Institute, Christchurch, New Zealand

    Richard D. Jones

  11. School of Psychology, Speech and Hearing at University of Canterbury, Christchurch, New Zealand

    Richard D. Jones

Authors
  1. Reza Shoorangiz
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  2. Stephen J. Weddell
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  3. Richard D. Jones
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Corresponding author

Correspondence to Reza Shoorangiz .

Editor information

Editors and Affiliations

  1. Neuroengineering and Biomedical Instrumentation Laboratory Department of Biomedical Engineering and Department of Electrical and Computer Engineering, Neurology, Johns Hopkins University, Baltimore, MD, USA

    Nitish V. Thakor

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© 2023 Springer Nature Singapore Pte Ltd.

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Shoorangiz, R., Weddell, S.J., Jones, R.D. (2023). EEG-Based Machine Learning: Theory and Applications. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-5540-1_70

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