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EEG Models and Analysis

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Handbook of Neuroengineering

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Abstract

Continuous recording of neural tissue registers extracellular potentials known as electroencephalogram or EEG. This type of electrical activity accounts for spatiotemporal synchronization of neuronal assemblies in the vicinity of the recording electrode. EEG has been linked to complex processes in the brain, such as sleep, epilepsy, Parkinson’s disease, memory, spatial navigation, and cognition, to name a few. Moreover, the number of applications involving neuronal oscillations has increased exponentially due to improved hardware, well-established recording paradigms, and better understanding of neural mechanisms. It is then necessary to analyze EEG in a principled framework with the proper rigor of statistical methods. The first part of this chapter details the neurophysiological basis of EEG-generating mechanisms at the cellular level, different types of recorded extracellular potentials (categorized by invasive level), and the well-established EEG rhythms alongside their main behavioral correlates. The second part of the chapter describes the statistical properties of EEG and how different methods address these constraints. The third section thoroughly details the mathematical models and analysis tools most commonly used to represent the spectral–temporal–spatial structure of EEG. Lastly, the chapter ends with a brief summary, discussion, and an outline of future directions. Overall, this chapter is intended as a literature review of the statistical treatment commonly applied to this signature brain activity.

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Abbreviations

AIC:

Akaike information criterion

AP:

Action potentials

AR:

Autoregressive

ARMA:

Autoregressive moving average

AWGN:

Additive white Gaussian noise

BCI:

Brain-computer interfaces

BIC:

Bayesian information criterion

BMI:

Brain-machine interfaces

DFT:

Discrete Fourier transform

EC:

Entorhinal cortex

ECG:

Electrocardiography

ECoG:

Electrocorticogram

EEG:

Electroencephalogram

EEGer:

Electroencephalographer

EMG:

Electromyography

EOG:

Electrooculography

EPSP:

Excitatory postsynaptic potentials

ERD:

Event-related desynchronization

ERS:

Event-related synchronization

FIR:

Finite impulse response

GABA:

Gamma-aminobutyric acid

ICA:

Independent component analysis

IPSP:

Inhibitory postsynaptic potentials

ISI:

Inter-spike interval

LFP:

Local field potential

LTI:

Linear, time invariant

LTP:

Long-term potentiation

MA:

Moving average

MEG:

Magnetoencephalogram

NREM:

Non-rapid eye movement

MSDB:

Medial septum diagonal band of Broca

PCA:

Principal component analysis

PSD:

Power spectral density

PSG:

Polysomnography

PSTH:

Peristimulus time histogram

RLS:

Recursive least squares

SNR:

Signal-to-noise ratio

SPP:

Simple point processes

SSS:

Strict sense stationarity

STFT:

Short-time Fourier transform

TF:

Time–frequency

WSS:

Wide sense stationarity

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Authors and Affiliations

  1. Department of Neuroscience, Center for Learning and Memory, The University of Texas at Austin, Austin, TX, USA

    Carlos A. Loza

  2. Computational NeuroEngineering Lab, Electrical and Computer Engineering Department, University of Florida, Gainesville, FL, USA

    Jose C. Principe

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  1. Carlos A. Loza
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  2. Jose C. Principe
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Correspondence to Carlos A. Loza .

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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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Loza, C.A., Principe, J.C. (2023). EEG Models and Analysis. In: Thakor, N.V. (eds) Handbook of Neuroengineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-5540-1_65

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