Differences in Power Spectral Densities and Phase Quantities Due to Processing of EEG Signals

There has been a growing interest in computational electroencephalogram (EEG) signal processing in a diverse set of domains, such as cortical excitability analysis, event-related synchronization, or desynchronization analysis. In recent years, several inconsistencies were found across different EEG studies, which authors often attributed to methodological differences. However, the assessment of such discrepancies is deeply underexplored. It is currently unknown if methodological differences can fully explain emerging differences and the nature of these differences. This study aims to contrast widely used methodological approaches in EEG processing and compare their effects on the outcome variables. To this end, two publicly available datasets were collected, each having unique traits so as to validate the results in two different EEG territories. The first dataset included signals with event-related potentials (visual stimulation) from 45 subjects. The second dataset included resting state EEG signals from 16 subjects. Five EEG processing steps, involved in the computation of power and phase quantities of EEG frequency bands, were explored in this study: artifact removal choices (with and without artifact removal), EEG signal transformation choices (raw EEG channels, Hjorth transformed channels, and averaged channels across primary motor cortex), filtering algorithms (Butterworth filter and Blackman–Harris window), EEG time window choices (−750 ms to 0 ms and −250 ms to 0 ms), and power spectral density (PSD) estimation algorithms (Welch’s method, Fast Fourier Transform, and Burg’s method). Powers and phases estimated by carrying out variations of these five methods were analyzed statistically for all subjects. The results indicated that the choices in EEG transformation and time-window can strongly affect the PSD quantities in a variety of ways. Additionally, EEG transformation and filter choices can influence phase quantities significantly. These results raise the need for a consistent and standard EEG processing pipeline for computational EEG studies. Consistency of signal processing methods cannot only help produce comparable results and reproducible research, but also pave the way for federated machine learning methods, e.g., where model parameters rather than data are shared.

[1]  F. Harris.  On the use of windows for harmonic analysis with the discrete Fourier transform , 1978, Proceedings of the IEEE.

[2]  James S. P. Macdonald,et al.  Trial-by-Trial Variations in Subjective Attentional State are Reflected in Ongoing Prestimulus EEG Alpha Oscillations , 2011, Front. Psychology.

[3]  H. Begleiter,et al.  Event related potentials during object recognition tasks , 1995, Brain Research Bulletin.

[4]  Michael C. Ridding,et al.  Transcranial Magnetic Stimulation-EEG Biomarkers of Poststroke Upper-Limb Motor Function. , 2019, Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association.

[5]  Logan Trujillo.  Raw EEG Data Files , 2019 .

[6]  J.B. Allen,et al.  A unified approach to short-time Fourier analysis and synthesis , 1977, Proceedings of the IEEE.

[7]  Heinrich Meyr,et al.  Digital filter and square timing recovery , 1988, IEEE Trans. Commun..

[8]  Taoguang Xu,et al.  Classical and modern power spectrum estimation for tune measurement in CSNS RCS , 2013, 1305.6090.

[9]  P. Rossini,et al.  Pre- and poststimulus alpha rhythms are related to conscious visual perception: a high-resolution EEG study. , 2005, Cerebral cortex.

[10]  S. Kak.  The discrete Hilbert transform , 1970 .

[11]  C. Neuper,et al.  EEG alpha band dissociation with increasing task demands. , 2005, Brain research. Cognitive brain research.

[12]  Paul Sauseng,et al.  EEG Oscillatory Phase-Dependent Markers of Corticospinal Excitability in the Resting Brain , 2014, BioMed research international.

[13]  Jun Lu,et al.  Adaptive Laplacian filtering for sensorimotor rhythm-based brain–computer interfaces , 2013, Journal of neural engineering.

[14]  Yan Guozheng,et al.  EEG feature extraction based on wavelet packet decomposition for brain computer interface , 2008 .

[15]  E Gordon,et al.  The Modulation of Late Component Event Related Potentials by Pre-Stimulus EEG Theta Activity in ADHD , 2001, The International journal of neuroscience.

[16]  Lorenzo Bruzzone,et al.  Automatic analysis of the difference image for unsupervised change detection , 2000, IEEE Trans. Geosci. Remote. Sens..

[17]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[18]  Lorenzo Fabrizi,et al.  Altered cortical processing of somatosensory input in pre-term infants who had high-grade germinal matrix-intraventricular haemorrhage , 2019, NeuroImage: Clinical.

[19]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[20]  M. Murugappan,et al.  Wireless EEG signals based Neuromarketing system using Fast Fourier Transform (FFT) , 2014, 2014 IEEE 10th International Colloquium on Signal Processing and its Applications.

[21]  J. Cadzow.  Maximum Entropy Spectral Analysis , 2006 .

[22]  N. Jaworska,et al.  Pre-treatment EEG signal variability is associated with treatment success in depression , 2017, NeuroImage: Clinical.

[23]  Ramaswamy Palaniappan,et al.  Neural Network Classification of Alcohol Abusers Using Power in Gamma Band Frequency of VEP Signals , 2003 .

[24]  Ramesh Srinivasan,et al.  Estimating the spatial Nyquist of the human EEG , 1998 .

[25]  Glyn Humphreys,et al.  Mu rhythm desynchronization reveals motoric influences of hand action on object recognition , 2013, Front. Hum. Neurosci..

[26]  Timothy O. West,et al.  Motor Cortex Inputs at the Optimum Phase of Beta Cortical Oscillations Undergo More Rapid and Less Variable Corticospinal Propagation , 2019, The Journal of Neuroscience.

[27]  Heinrich Garn,et al.  Neuroimaging markers of global cognition in early Alzheimer's disease: A magnetic resonance imaging–electroencephalography study , 2018, Brain and behavior.

[28]  S. Makeig,et al.  Imaging human EEG dynamics using independent component analysis , 2006, Neuroscience & Biobehavioral Reviews.

[29]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[30]  A. Mognon,et al.  ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. , 2011, Psychophysiology.

[31]  Nandini Jog,et al.  Classification of Artefacts in EEG Signal Recordings and EOG Artefact Removal using EOG Subtraction , 2016 .

[32]  P. Rossini,et al.  “Small World” architecture in brain connectivity and hippocampal volume in Alzheimer’s disease: a study via graph theory from EEG data , 2016, Brain Imaging and Behavior.

[33]  Aaron Schurger,et al.  Pre-stimulus theta power is correlated with variation of motor evoked potential latency: a single-pulse TMS study , 2018, Experimental Brain Research.

[34]  U. Rajendra Acharya,et al.  Entropies for detection of epilepsy in EEG , 2005, Comput. Methods Programs Biomed..

[35]  K. Spencer,et al.  Poststimulus EEG spectral analysis and P300: attention, task, and probability. , 1999, Psychophysiology.

[36]  Rabab K Ward,et al.  A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals , 2007, Journal of neural engineering.

[37]  Logan T. Trujillo.  K-th Nearest Neighbor (KNN) Entropy Estimates of Complexity and Integration from Ongoing and Stimulus-Evoked Electroencephalographic (EEG) Recordings of the Human Brain , 2019, Entropy.

[38]  W. Klimesch.  EEG-alpha rhythms and memory processes. , 1997, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[39]  Kay A. Robbins,et al.  How Sensitive are EEG Results to Preprocessing Methods: A Benchmarking Study , 2020, bioRxiv.

[40]  Caley Sullivan,et al.  Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open-source TESA software , 2017, NeuroImage.

[41]  T. Hasan.  Complex demodulation: Some theory and applications , 1983 .

[42]  G Fein,et al.  Common reference coherence data are confounded by power and phase effects. , 1988, Electroencephalography and clinical neurophysiology.

[43]  R. Acharya,et al.  Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques , 2008 .

[44]  Mohammad Hassan Moradi,et al.  A new approach for EEG feature extraction in P300-based lie detection , 2009, Comput. Methods Programs Biomed..

[45]  P. Fitzgerald,et al.  Impact of different intensities of intermittent theta burst stimulation on the cortical properties during TMS‐EEG and working memory performance , 2018, Human brain mapping.

[46]  Lavanya Krishna,et al.  Prediction of Musical Perception using EEG and Functional Connectivity in the Brain , 2018 .

[47]  Benedict Shien Wei Ng,et al.  EEG phase patterns reflect the selectivity of neural firing. , 2013, Cerebral cortex.

[48]  Jonathan R Wolpaw,et al.  Sensorimotor rhythm-based brain–computer interface (BCI): model order selection for autoregressive spectral analysis , 2008, Journal of neural engineering.

[49]  Terence O'Brien,et al.  Epileptic Seizure Detection Using Convolutional Neural Network: A Multi-Biosignal study , 2020, ACSW.

[50]  M. Brandt,et al.  Pre-stimulus spectral EEG patterns and the visual evoked response. , 1991, Electroencephalography and clinical neurophysiology.

[51]  Levin Kuhlmann,et al.  Computationally Efficient Epileptic Seizure Prediction based on Extremely Randomised Trees , 2020, ACSW.

[52]  H. Petsche,et al.  Phase-coupling of theta-gamma EEG rhythms during short-term memory processing. , 2002, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[53]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[54]  B. MCA. SAVERS,et al.  The Mechanism of Auditory Evoked EEG Responses , 1974, Nature.

[55]  Claudio Carvalhaes,et al.  The surface Laplacian technique in EEG: Theory and methods. , 2014, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[56]  R. Thatcher,et al.  EEG and intelligence: Relations between EEG coherence, EEG phase delay and power , 2005, Clinical Neurophysiology.

[57]  D.J. McFarland,et al.  An Evaluation of Autoregressive Spectral Estimation Model Order for Brain-Computer Interface Applications , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[58]  P. Ktonas,et al.  Instantaneous envelope and phase extraction from real signals: Theory, implementation, and an application to EEG analysis , 1980 .

[59]  Rifai Chai,et al.  Comparing the Effects of Three Cognitive Tasks on Indicators of Mental Fatigue , 2019, The Journal of psychology.

[60]  Dimitrios I. Fotiadis,et al.  Epileptic Seizure Detection in EEGs Using Time–Frequency Analysis , 2009, IEEE Transactions on Information Technology in Biomedicine.

[61]  Alan R. Jones,et al.  Fast Fourier Transform , 1970, SIGP.

[62]  Georg Northoff,et al.  Atypical Temporal Dynamics of Resting State Shapes Stimulus-Evoked Activity in Depression—An EEG Study on Rest–Stimulus Interaction , 2019, Front. Psychiatry.

[63]  Olaf Dimigen,et al.  Optimizing the ICA-based removal of ocular EEG artifacts from free viewing experiments , 2020, NeuroImage.

[64]  Claudio Del Percio,et al.  Cortical alpha rhythms are correlated with body sway during quiet open-eyes standing in athletes: A high-resolution EEG study , 2007, NeuroImage.

[65]  L. Litwin.  FIR and IIR digital filters , 2000 .

[66]  B. Hjorth.  An on-line transformation of EEG scalp potentials into orthogonal source derivations. , 1975, Electroencephalography and clinical neurophysiology.

[67]  Hanni Kiiski,et al.  A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment , 2019, NeuroImage.

[68]  Christoph Zrenner,et al.  Sensorimotor Oscillatory Phase-Power Interaction Gates Resting Human Corticospinal Output. , 2018, Cerebral cortex.