Waveform complexity: A new metric for EEG analysis

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Waveform complexity : A new metric for EEG analysis. / Parameshwaran, Dhanya; Subramaniyam, Narayan P.; Thiagarajan, Tara C.

julkaisussa: Journal of Neuroscience Methods, Vuosikerta 325, 108313, 01.09.2019.

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Parameshwaran, Dhanya ; Subramaniyam, Narayan P. ; Thiagarajan, Tara C. / Waveform complexity : A new metric for EEG analysis. Julkaisussa: Journal of Neuroscience Methods. 2019 ; Vuosikerta 325.

Bibtex - Lataa

@article{ef246f9366d7461f988a3c5483df886d,
title = "Waveform complexity: A new metric for EEG analysis",
abstract = "Background: EEG represents a cost-effective mechanism to evaluate brain function. To realize its potential, it is essential to identify aspects of the signal that provide insight into differences in cognitive, emotional and behavioral outcomes and can therefore aid in diagnostic measurement. Here we define a new metric of the EEG signal that assesses the diversity of waveform shapes in the signal. New method: The metric, which we term waveform complexity, abbreviated as Cw, compares the similarity of the shape of waveforms of long durations by computing the correlation (r) of segments. A distribution of waveform diversity is computed as 1-|r|x100, from which Cw is obtained as the median. Results: We identify the length parameter that provides the maximal variance in Cw across the sample population and therefore greatest potential discriminatory power. We also provide insight into the impact of various manipulations of the signal such as sampling rate, filtering, phase shuffling and signal duration. Finally, as a test of potential application, we demonstrate that when applied to eyes closed EEG recordings in subjects taken immediately prior to taking a Raven's progressive matrix test, this measure had a high correlation to participant's scores. Comparison with existing methods: Cw, while correlated with other similar measures such as spectral entropy, sample entropy and Lempel-Ziv complexity, significantly outperformed these measures in its correlation to participants’ task scores. Conclusions: This waveform complexity measure warrants further investigation as a potential measure of cognitive and other brain states.",
keywords = "Complexity, EEG",
author = "Dhanya Parameshwaran and Subramaniyam, {Narayan P.} and Thiagarajan, {Tara C.}",
year = "2019",
month = "9",
day = "1",
doi = "10.1016/j.jneumeth.2019.108313",
language = "English",
volume = "325",
journal = "Journal of Neuroscience Methods",
issn = "0165-0270",
publisher = "Elsevier",

}

RIS - Lataa

TY - JOUR

T1 - Waveform complexity

T2 - A new metric for EEG analysis

AU - Parameshwaran, Dhanya

AU - Subramaniyam, Narayan P.

AU - Thiagarajan, Tara C.

PY - 2019/9/1

Y1 - 2019/9/1

N2 - Background: EEG represents a cost-effective mechanism to evaluate brain function. To realize its potential, it is essential to identify aspects of the signal that provide insight into differences in cognitive, emotional and behavioral outcomes and can therefore aid in diagnostic measurement. Here we define a new metric of the EEG signal that assesses the diversity of waveform shapes in the signal. New method: The metric, which we term waveform complexity, abbreviated as Cw, compares the similarity of the shape of waveforms of long durations by computing the correlation (r) of segments. A distribution of waveform diversity is computed as 1-|r|x100, from which Cw is obtained as the median. Results: We identify the length parameter that provides the maximal variance in Cw across the sample population and therefore greatest potential discriminatory power. We also provide insight into the impact of various manipulations of the signal such as sampling rate, filtering, phase shuffling and signal duration. Finally, as a test of potential application, we demonstrate that when applied to eyes closed EEG recordings in subjects taken immediately prior to taking a Raven's progressive matrix test, this measure had a high correlation to participant's scores. Comparison with existing methods: Cw, while correlated with other similar measures such as spectral entropy, sample entropy and Lempel-Ziv complexity, significantly outperformed these measures in its correlation to participants’ task scores. Conclusions: This waveform complexity measure warrants further investigation as a potential measure of cognitive and other brain states.

AB - Background: EEG represents a cost-effective mechanism to evaluate brain function. To realize its potential, it is essential to identify aspects of the signal that provide insight into differences in cognitive, emotional and behavioral outcomes and can therefore aid in diagnostic measurement. Here we define a new metric of the EEG signal that assesses the diversity of waveform shapes in the signal. New method: The metric, which we term waveform complexity, abbreviated as Cw, compares the similarity of the shape of waveforms of long durations by computing the correlation (r) of segments. A distribution of waveform diversity is computed as 1-|r|x100, from which Cw is obtained as the median. Results: We identify the length parameter that provides the maximal variance in Cw across the sample population and therefore greatest potential discriminatory power. We also provide insight into the impact of various manipulations of the signal such as sampling rate, filtering, phase shuffling and signal duration. Finally, as a test of potential application, we demonstrate that when applied to eyes closed EEG recordings in subjects taken immediately prior to taking a Raven's progressive matrix test, this measure had a high correlation to participant's scores. Comparison with existing methods: Cw, while correlated with other similar measures such as spectral entropy, sample entropy and Lempel-Ziv complexity, significantly outperformed these measures in its correlation to participants’ task scores. Conclusions: This waveform complexity measure warrants further investigation as a potential measure of cognitive and other brain states.

KW - Complexity

KW - EEG

UR - http://www.scopus.com/inward/record.url?scp=85068565645&partnerID=8YFLogxK

U2 - 10.1016/j.jneumeth.2019.108313

DO - 10.1016/j.jneumeth.2019.108313

M3 - Article

AN - SCOPUS:85068565645

VL - 325

JO - Journal of Neuroscience Methods

JF - Journal of Neuroscience Methods

SN - 0165-0270

M1 - 108313

ER -

ID: 35440893