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Brain Science Advances 2020, 6(3): 189-209 https://doi.org/10.26599/BSA.2020.9050019
Review Article | Open Access | Issue | Published: 04 February 2021

Demystifying signal processing techniques to extract resting- state EEG features for psychologists

Show Author's Information Zhenjiang Li1,§,  Libo Zhang2,3,§,  Fengrui Zhang2,3,  Ruolei Gu3,4,  Weiwei Peng5,  Li Hu2,3( )
School of Psychology, Jiangxi Normal University, Nanchang 330022, Jiangxi, China
CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, China
Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
College of Psychology and Sociology, Shenzhen University, Shenzhen 518060, China

§ These authors contributed equally to this work.

Keywords:
preprocessing, resting-state EEG, spectral analysis, connectivity analysis, microstate analysis
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Li Z, Zhang L, Zhang F, et al. Demystifying signal processing techniques to extract resting- state EEG features for psychologists. Brain Science Advances, 2020, 6(3): 189-209. https://doi.org/10.26599/BSA.2020.9050019
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Abstract Full text About this article

Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non-invasively. Some important mental functions could be encoded by resting-state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting-state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting-state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting-state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft-used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.


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About this article

Demystifying signal processing techniques to extract resting- state EEG features for psychologists

Show Author's information Zhenjiang Li1,§, Libo Zhang2,3,§, Fengrui Zhang2,3, Ruolei Gu3,4, Weiwei Peng5, Li Hu2,3( )
School of Psychology, Jiangxi Normal University, Nanchang 330022, Jiangxi, China
CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, China
Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
College of Psychology and Sociology, Shenzhen University, Shenzhen 518060, China

§ These authors contributed equally to this work.

Abstract

Electroencephalography (EEG) is a powerful tool for investigating the brain bases of human psychological processes non-invasively. Some important mental functions could be encoded by resting-state EEG activity; that is, the intrinsic neural activity not elicited by a specific task or stimulus. The extraction of informative features from resting-state EEG requires complex signal processing techniques. This review aims to demystify the widely used resting-state EEG signal processing techniques. To this end, we first offer a preprocessing pipeline and discuss how to apply it to resting-state EEG preprocessing. We then examine in detail spectral, connectivity, and microstate analysis, covering the oft-used EEG measures, practical issues involved, and data visualization. Finally, we briefly touch upon advanced techniques like nonlinear neural dynamics, complex networks, and machine learning.

Keywords: preprocessing, resting-state EEG, spectral analysis, connectivity analysis, microstate analysis

References(81)

[1]
RJ Gerrig. Psychology and Life. 20th ed. Boston, USA: Pearson, 2013.
[2]
R Caton. Electrical currents of the brain. Br Med J. 1875, 2: 278.
Google Scholar
[3]
H Berger. Über das Elektrenkephalogramm des Menschen. Archiv F Psychiatrie. 1937, 106(1): 577-584.
Google Scholar
[4]
SJ Luck. An Introduction to the Event-Related Potential Technique. 2nd ed. Cambridge, MA, USA: MIT Press, 2014.
[5]
ED Adrian, BHC Matthews. The Berger rhythm: potential changes from the occipital lobes in man. Brain. 1934, 57(4): 355-385.
Google Scholar
[6]
HH Jasper, L Carmichael. Electrical potentials from the intact human brain. Science. 1935, 81(2089): 51-53.
Google Scholar
[7]
FA Gibbs, H Davis, WG Lennox. The electro- encephalogram in epilepsy and in conditions of impaired consciousness. Arch NeurPsych. 1935, 34(6): 1133-1148.
Google Scholar
[8]
MX Cohen. Analyzing Neural Time Series Data: Theory and Practice. Cambridge, MA, USA: MIT Press, 2014.
[9]
X Xia, L Hu. EEG: neural basis and measurement. In EEG Signal Processing and Feature Extraction. L Hu, Z Zhang, Eds. Singapore: Springer Singapore, 2019.
[10]
S Murakami, Y Okada. Contributions of principal neocortical neurons to magnetoencephalography and electroencephalography signals. J Physiol. 2006, 575(pt 3): 925-936.
Google Scholar
[11]
NK Logothetis. The underpinnings of the BOLD functional magnetic resonance imaging signal. J Neurosci. 2003, 23(10): 3963-3971.
Google Scholar
[12]
L Hu, GD Iannetti. Neural indicators of perceptual variability of pain across species. Proc Natl Acad Sci USA. 2019, 116(5): 1782-1791.
Google Scholar
[13]
M Ploner, ES May. EEG and MEG in pain research - Current state and future perspectives. Pain. 2017, 159(2): 206.
Google Scholar
[14]
V Romei, J Gross, G Thut. On the role of prestimulus alpha rhythms over occipito-parietal areas in visual input regulation: correlation or causation? J Neurosci. 2010, 30(25): 8692-8697.
Google Scholar
[15]
A Tomassini, D Spinelli, M Jacono, et al. Rhythmic oscillations of visual contrast sensitivity synchronized with action. J Neurosci. 2015, 35(18): 7019-7029.
Google Scholar
[16]
M Wöstmann, M Alavash, J Obleser. Alpha oscillations in the human brain implement distractor suppression independent of target selection. J Neurosci. 2019, 39(49): 9797-9805.
Google Scholar
[17]
EK Vogel, AW McCollough, MG Machizawa. Neural measures reveal individual differences in controlling access to working memory. Nature. 2005, 438(7067): 500-503.
Google Scholar
[18]
R Polanía, I Krajbich, M Grueschow, et al. Neural oscillations and synchronization differentially support evidence accumulation in perceptual and value-based decision making. Neuron. 2014, 82(3): 709-720.
Google Scholar
[19]
S Weiss, HM Mueller. The contribution of EEG coherence to the investigation of language. Brain Lang. 2003, 85(2): 325-343.
Google Scholar
[20]
TA Dennis, B Solomon. Frontal EEG and emotion regulation: electrocortical activity in response to emotional film clips is associated with reduced mood induction and attention interference effects. Biol Psychol. 2010, 85(3): 456-464.
Google Scholar
[21]
E Hehman, HI Volpert, RF Simons. The N400 as an index of racial stereotype accessibility. Soc Cogn Affect Neurosci. 2014, 9(4): 544-552.
Google Scholar
[22]
CD Cameron, BK Payne, W Sinnott-Armstrong, et al. Implicit moral evaluations: a multinomial modeling approach. Cognition. 2017, 158: 224-241.
Google Scholar
[23]
AZ Snyder, ME Raichle. A brief history of the resting state: the Washington University perspective. Neuroimage. 2012, 62(2): 902-910.
Google Scholar
[24]
C Babiloni, RJ Barry, E Başar, et al. International Federation of Clinical Neurophysiology (IFCN) - EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies. Clin Neurophysiol. 2020, 131(1): 285-307.
Google Scholar
[25]
TH Grandy, M Werkle-Bergner, C Chicherio, et al. Individual alpha peak frequency is related to latent factors of general cognitive abilities. Neuroimage. 2013, 79: 10-18.
Google Scholar
[26]
W Peng. EEG preprocessing and denoising. In EEG Signal Processing and Feature Extraction. L Hu, Z Zhang, Eds. Singapore: Springer Singapore, 2019.
[27]
A Delorme, S Makeig. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004, 134(1): 9-21.
Google Scholar
[28]
S Hu, Y Lai, PA Valdes-Sosa, et al. How do reference montage and electrodes setup affect the measured scalp EEG potentials? J Neural Eng. 2018, 15(2): 026013.
Google Scholar
[29]
RT Pivik, RJ Broughton, R Coppola, et al. Guidelines for the recording and quantitative analysis of electroencephalographic activity in research contexts. Psychophysiology. 1993, 30(6): 547-558.
Google Scholar
[30]
DZ Yao, Y Qin, SA Hu, et al. Which reference should we use for EEG and ERP practice? Brain Topogr. 2019, 32(4): 530-549.
Google Scholar
[31]
J Dien. Issues in the application of the average reference: Review, critiques, and recommendations. Behav Res Methods Instruments Comput. 1998, 30(1): 34-43.
Google Scholar
[32]
M Junghöfer, T Elbert, DM Tucker, et al. The polar average reference effect: a bias in estimating the head surface integral in EEG recording. Clin Neurophysiol. 1999, 110(6): 1149-1155.
Google Scholar
[33]
M Teplan. Fundamentals of EEG measurement. Meas Sci Rev. 2002, 2(2): 1-11.
Google Scholar
[34]
D Yao. A method to standardize a reference of scalp EEG recordings to a point at infinity. Physiol Meas. 2001, 22(4): 693-711.
Google Scholar
[35]
D Yao, L Wang, R Oostenveld, et al. A comparative study of different references for EEG spectral mapping: the issue of the neutral reference and the use of the infinity reference. Physiol Meas. 2005, 26(3): 173-184.
Google Scholar
[36]
WJ Freeman, RQ Quiroga. Imaging brain function with EEG. New York, NY, USA: Springer New York, 2013.
[37]
LL Greischar, CA Burghy, CM van Reekum, et al. Effects of electrode density and electrolyte spreading in dense array electroencephalographic recording. Clin Neurophysiol. 2004, 115(3): 710-720.
Google Scholar
[38]
Y Zou, V Nathan, R Jafari. Automatic identification of artifact-related independent components for artifact removal in EEG recordings. IEEE J Biomed Health Inform. 2016, 20(1): 73-81.
Google Scholar
[39]
S Makeig, S Debener, J Onton, et al. Mining event-related brain dynamics. Trends Cogn Sci. 2004, 8(5): 204-210.
Google Scholar
[40]
M Chaumon, DV Bishop, NA Busch. A practical guide to the selection of independent components of the electroencephalogram for artifact correction. J Neurosci Methods. 2015, 250: 47-63.
Google Scholar
[41]
N Kane, J Acharya, S Benickzy, et al. A revised glossary of terms most commonly used by clinical electroencephalographers and updated proposal for the report format of the EEG findings. Revision 2017. Clin Neurophysiol Pract. 2017, 2: 170-185.
Google Scholar
[42]
PL Nunez, R Srinivasan. Electric Fields of the Brain. 2nd ed. New York, USA: Oxford University Press, 2006.
[43]
Z Zhang. Spectral and time-frequency analysis. In EEG Signal Processing and Feature Extraction. L Hu, Z Zhang, Eds. Singapore: Springer Singapore, 2019.
[44]
JW Cooley, JW Tukey. An algorithm for the machine calculation of complex Fourier series. Math Comp. 1965, 19(90): 297.
Google Scholar
[45]
P Welch. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoustics. 1967, 15(2): 70-73.
Google Scholar
[46]
MS Bartlett. An Introduction to Stochastic Processes: with Special Reference to Methods and Applications. 3rd ed. Cambridge, MA, USA: Cambridge University Press, 1978.
[47]
DA Abrams, CJ Lynch, KM Cheng, et al. Underconnectivity between voice-selective cortex and reward circuitry in children with autism. Proc Natl Acad Sci USA. 2013, 110(29): 12060-12065.
Google Scholar
[48]
GM Ibrahim, R Anderson, T Akiyama, et al. Neocortical pathological high-frequency oscillations are associated with frequency-dependent alterations in functional network topology. J Neurophysiol. 2013, 110(10): 2475-2483.
Google Scholar
[49]
S Dubovik, A Bouzerda-Wahlen, L Nahum, et al. Adaptive reorganization of cortical networks in Alzheimer's disease. Clin Neurophysiol. 2013, 124(1): 35-43.
Google Scholar
[50]
N Fogelson, L Li, Y Li, et al. Functional connectivity abnormalities during contextual processing in schizophrenia and in Parkinson's disease. Brain Cogn. 2013, 82(3): 243-253.
Google Scholar
[51]
AM Aertsen, GL Gerstein, MK Habib, et al. Dynamics of neuronal firing correlation: modulation of “effective connectivity”. J Neurophysiol. 1989, 61(5): 900-917.
Google Scholar
[52]
G Niso, R Bruña, E Pereda, et al. HERMES: towards an integrated toolbox to characterize functional and effective brain connectivity. Neuroinformatics. 2013, 11(4): 405-434.
Google Scholar
[53]
L Hu, E Valentini, ZG Zhang, et al. The primary somatosensory cortex contributes to the latest part of the cortical response elicited by nociceptive somatosensory stimuli in humans. Neuroimage. 2014, 84: 383-393.
Google Scholar
[54]
JP Lachaux, E Rodriguez, J Martinerie, et al. Measuring phase synchrony in brain signals. Hum Brain Mapp. 1999, 8(4): 194-208.
Google Scholar
[55]
CJ Stam, G Nolte, A Daffertshofer. Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp. 2007, 28(11): 1178-1193.
Google Scholar
[56]
E van Diessen, T Numan, E van Dellen, et al. Opportunities and methodological challenges in EEG and MEG resting state functional brain network research. Clin Neurophysiol. 2015, 126(8): 1468-1481.
Google Scholar
[57]
M Vinck, R Oostenveld, M van Wingerden, et al. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage. 2011, 55(4): 1548-1565.
Google Scholar
[58]
KJ Blinowska. Review of the methods of determination of directed connectivity from multichannel data. Med Biol Eng Comput. 2011, 49(5): 521-529.
Google Scholar
[59]
J Pearl, M Glymour, NP Jewell. Causal Inference in Statistics: A Primer. Chichester, UK: Wiley, 2016.
[60]
CWJ Granger. Investigating causal relations by econometric models and cross-spectral methods. Econometrica. 1969, 37(3): 424-438.
Google Scholar
[61]
H Jia. Connectivity analysis. In EEG Signal Processing and Feature Extraction. L Hu, Z Zhang, Eds. Singapore: Springer Singapore, 2019.
[62]
D Lehmann, H Ozaki, I Pal. EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalogr Clin Neurophysiol. 1987, 67(3): 271-288.
Google Scholar
[63]
RD Pascual-Marqui, CM Michel, D Lehmann. Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Trans Biomed Eng. 1995, 42(7): 658-665.
Google Scholar
[64]
D Lehmann, R Pascual-Marqui, C Michel. EEG microstates. Scholarpedia. 2009, 4(3): 7632.
Google Scholar
[65]
A Khanna, A Pascual-Leone, CM Michel, et al. Microstates in resting-state EEG: current status and future directions. Neurosci Biobehav Rev. 2015, 49: 105-113.
Google Scholar
[66]
J Britz, D Van De Ville, CM Michel. BOLD correlates of EEG topography reveal rapid resting-state network dynamics. Neuroimage. 2010, 52(4): 1162-1170.
Google Scholar
[67]
MM Murray, D Brunet, CM Michel. Topographic ERP analyses: a step-by-step tutorial review. Brain Topogr. 2008, 20(4): 249-264.
Google Scholar
[68]
D Lehmann, W Skrandies. Reference-free identification of components of checkerboard-evoked multichannel potential fields. Electroencephalogr Clin Neurophysiol. 1980, 48(6): 609-621.
Google Scholar
[69]
D Brunet, MM Murray, CM Michel. Spatiotemporal analysis of multichannel EEG: CARTOOL. Comput Intell Neurosci. 2011, 2011: 813870.
Google Scholar
[70]
H Jia. Microstate analysis. In EEG Signal Processing and Feature Extraction. L Hu, Z Zhang, Eds. Singapore: Springer Singapore, 2019.
[71]
CM Michel, T Koenig. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review. Neuroimage. 2018, 180(pt b): 577-593.
Google Scholar
[72]
Y Bai, X Li, Z Liang. Nonlinear neural dynamics. In EEG Signal Processing and Feature Extraction. L Hu, Z Zhang, Eds. Singapore: Springer Singapore, 2019.
[73]
S Sanei, J Chambers. EEG Signal Processing. West Sussex, UK: John Wiley & Sons, 2007.
[74]
A Lempel, J Ziv. On the complexity of finite sequences. IEEE Trans Inform Theory. 1976, 22(1): 75-81.
Google Scholar
[75]
CE Shannon. A mathematical theory of communication. Bell Syst Tech J. 1948, 27(3): 379-423.
Google Scholar
[76]
M Rubinov, SA Knock, CJ Stam, et al. Small-world properties of nonlinear brain activity in schizophrenia. Hum Brain Mapp. 2009, 30(2): 403-416.
Google Scholar
[77]
RJ Trudeau. Introduction to Graph Theory. 2nd ed. New York, USA: Dover Publications, 1994.
[78]
L Hu, ZG Zhang. EEG signal processing and feature extraction[M]. Singapore: Springer Singapore, 2019.
[79]
D Wen, Z Wei, Y Zhou, et al. Spatial complex brain network. In EEG Signal Processing and Feature Extraction. L Hu, Z Zhang, Eds. Singapore: Springer Singapore, 2019.
[80]
Y Tu. Machine learning. In EEG Signal Processing and Feature Extraction. L Hu, Z Zhang, Eds. Singapore: Springer Singapore, 2019.
[81]
G Huang, P Xiao, YS Hung, et al. A novel approach to predict subjective pain perception from single-trial laser-evoked potentials. Neuroimage. 2013, 81: 283-293.
Google Scholar
Publication history
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Publication history

Received: 05 June 2020
Revised: 14 July 2020
Accepted: 23 July 2020
Published: 04 February 2021
Issue date: September 2020

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© The authors 2020

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 31822025, No. 31671141). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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