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Deep Learning Methods for EEG Neural Classification

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Handbook of Neuroengineering

Abstract

Classification of patterns of brain activity in neuroengineering research is an important tool for understanding the brain, developing neurodiagnostics, and designing closed-loop neural interfaces. Scalp electroencephalography (EEG), by virtue of its noninvasiveness and lower cost, has been used for neural signal classification, and researchers have utilized various machine learning methods. Recently, deep learning has gained popularity due to its ability to significantly increase the classification performance in numerous domains while elucidating the relevant features for classification. It is a natural step to deploy such promising techniques for EEG classification tasks. This book chapter aims to serve as a comprehensive reference source for both EEG and deep learning researchers interested in EEG-based deep learning studies. Potential pitfalls, challenges, and opportunities in the application of deep learning to EEG data are discussed.

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Abbreviations

AD:

Alzheimer’s Disease

AE:

AutoEncoder

AMIGOS:

Dataset for Affect, Personality, and Mood Research on Individuals and Groups

BCI:

Brain Computer Interface

BIDS:

Brain Imaging Data Structure

BLDA:

Bayesian Linear Discriminant Analysis

CCNN:

Channel-wise CNN

CNN:

Convolutional Neural Network

CNN-R:

Residual CNN

CSP:

Common Spatial Pattern

DBN:

Deep Belief Machine

DBS:

Deep Brain Stimulation

DEAP:

Database of Emotion Analysis using Physiological signals

DL:

Deep Learning

DSP:

Digital Signal Processing

EEG:

Electroencephalography

ELU:

Exponential Linear Unit

EMG:

Electromyography

EOG:

Electrooculography

ERN:

Error-related Negativity Response

ERP:

Event Related Potential

FFT:

Fast Fourier Transform

GRU:

Gated Recurrent Unit

HDCA:

Hierarchical Discriminant Component Analysis

ITR:

Information Transfer Rate

KMI:

Kinesthetic Motor Imagery

LDA:

Linear Discriminant Analysis

LRP:

Layer-wise Relevance Propagation

LSTM:

Long-Short Term Memory

LVQ:

Linear Vector Quantization

MASS:

Montreal Archive of Sleep Studies

MCI:

Motor Cognitive Impairment

MDM:

Minimum Distance to Mean

ML:

Machine Learning

MLP:

Multi-Layer Perceptron

MRCP:

Movement-Related Cortical Potential

NIH:

National Institutes of Health

NN:

Neural Network

PCA:

Principal Component Analysis

RBM:

Restricted Boltzmann Machine

RCNN:

Recurrent CNN

ReLU:

Rectified Linear Unit

REM:

Rapid Eye Movement

RNN:

Recurrent Neural Networks

RSVP:

Rapid Serial Visual Presentation

SEED:

SJTU Emotion EEG Database

SELU:

Scaled Exponential Linear Unit

SMR:

Sensory-Motor Rhythms

SNR:

Signal-to-Noise Ratio

SOTA:

State of the Art

SSVEP:

Steady-State Visual Evoked Potential

SVM:

Support Vector Machines

TCN:

Temporal Convolution Network

TSNN:

Two Stream Neural Network

VMI:

Visual Motor Imagery

WoS:

Web of Science

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Authors and Affiliations

  1. Laboratory for Noninvasive Brain-Machine Interface Systems, IUCRC BRAIN, University of Houston, Houston, TX, USA

    Sho Nakagome, Alexander Craik, Akshay Sujatha Ravindran, Yongtian He, Jesus G. Cruz-Garza & Jose L. Contreras-Vidal

Authors
  1. Sho Nakagome
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  2. Alexander Craik
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  3. Akshay Sujatha Ravindran
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  4. Yongtian He
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  5. Jesus G. Cruz-Garza
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  6. Jose L. Contreras-Vidal
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Corresponding author

Correspondence to Jose L. Contreras-Vidal .

Editor information

Editors and Affiliations

  1. Singapore Institute for Neurotechnology, National University of Singapore, Singapore, Singapore

    Nitish V. Thakor

Section Editor information

  1. N.1. Institute for Health, National University of Singapore, Singapore, Singapore

    Andrei Dragomir

  2. Department of Biomedical Engineering, University of Houston, Houston, USA

    Andrei Dragomir

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Nakagome, S., Craik, A., Sujatha Ravindran, A., He, Y., Cruz-Garza, J.G., Contreras-Vidal, J.L. (2022). Deep Learning Methods for EEG Neural Classification. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-2848-4_78-1

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  • DOI: https://doi.org/10.1007/978-981-15-2848-4_78-1

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