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Xu X, Kong Q, Zhang D, Zhang Y. An evaluation of inter-brain EEG coupling methods in hyperscanning studies. Cogn Neurodyn 2024; 18:67-83. [PMID: 38406199 PMCID: PMC10881924 DOI: 10.1007/s11571-022-09911-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 10/24/2022] [Accepted: 10/31/2022] [Indexed: 11/28/2022] Open
Abstract
EEG-based hyperscanning technology has been increasingly applied to analyze interpersonal interactions in social neuroscience in recent years. However, different methods are employed in various of studies without a complete investigation of the suitability of these methods. Our study aimed to systematically compare typical inter-brain EEG coupling methods, with simulated EEG data generated by real EEG data. In particular, two critical metrics of noise level and time delay were manipulated, and three different coupling models were tested. The results revealed that: (1) under certain conditions, various methods were leveraged by noise level and time delay, leading to different performances; (2) most algorithms achieved better experimental results and performance under high coupling degree; (3) with our simulation process, temporal and spectral models showed relatively good results, while data simulated with phase coupling model performed worse. This is the first systematic comparison of typical inter-brain EEG coupling methods, with simulated EEG data generated by real EEG data from different subjects. Existing methods mainly focused on intra-brain coupling. To our knowledge, there was only one previous study that compared five inter-brain EEG coupling methods (Burgess in Front Human Neurosci 7:881, 2013). However, the simulated data used in this study were generated time series with varied degrees of phase coupling without considering any EEG characteristics. For future research, appropriate methods need to be selected based on possible underlying mechanisms (temporal, spectral and phase coupling model hypothesis) of a specific study, as well as the expected coupling degree and conditions. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09911-1.
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Affiliation(s)
- Xiaomeng Xu
- Institute of Education, Tsinghua University, Beijing, China
| | - Qiuyue Kong
- School of Public Health, Harvard University, Cambridge, MA USA
| | - Dan Zhang
- Department of Psychology, Tsinghua University, Beijing, China
| | - Yu Zhang
- Institute of Education, Tsinghua University, Beijing, China
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2
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Sun J, Jia T, Lin PJ, Li Z, Ji L, Li C. Multiscale Canonical Coherence for Functional Corticomuscular Coupling Analysis. IEEE J Biomed Health Inform 2024; 28:812-822. [PMID: 37963005 DOI: 10.1109/jbhi.2023.3332657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
Functional corticomuscular coupling (FCMC) probes multi-level information communication in the sensorimotor system. The canonical Coherence (caCOH) method has been leveraged to measure the FCMC between two multivariate signals within the single scale. In this paper, we propose the concept of multiscale canonical Coherence (MS-caCOH) to disentangle complex multi-layer information and extract coupling features in multivariate spaces from multiple scales. Then, we verified the reliability and effectiveness of MS-caCOH on two types of data sets, i.e., a synthetic multivariate data set and a real-world multivariate data set. Our simulation results showed that compared with caCOH, MS-caCOH enhanced coupling detection and achieved lower pattern recovery errors at multiple frequency scales. Further analysis on experimental data demonstrated that the proposed MS-caCOH method could also capture detailed multiscale spatial-frequency characteristics. This study leverages the multiscale analysis framework and multivariate method to give a new insight into corticomuscular coupling analysis.
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Barnova K, Mikolasova M, Kahankova RV, Jaros R, Kawala-Sterniuk A, Snasel V, Mirjalili S, Pelc M, Martinek R. Implementation of artificial intelligence and machine learning-based methods in brain-computer interaction. Comput Biol Med 2023; 163:107135. [PMID: 37329623 DOI: 10.1016/j.compbiomed.2023.107135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/13/2023] [Accepted: 06/04/2023] [Indexed: 06/19/2023]
Abstract
Brain-computer interfaces are used for direct two-way communication between the human brain and the computer. Brain signals contain valuable information about the mental state and brain activity of the examined subject. However, due to their non-stationarity and susceptibility to various types of interference, their processing, analysis and interpretation are challenging. For these reasons, the research in the field of brain-computer interfaces is focused on the implementation of artificial intelligence, especially in five main areas: calibration, noise suppression, communication, mental condition estimation, and motor imagery. The use of algorithms based on artificial intelligence and machine learning has proven to be very promising in these application domains, especially due to their ability to predict and learn from previous experience. Therefore, their implementation within medical technologies can contribute to more accurate information about the mental state of subjects, alleviate the consequences of serious diseases or improve the quality of life of disabled patients.
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Affiliation(s)
- Katerina Barnova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Martina Mikolasova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Radana Vilimkova Kahankova
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia
| | - Rene Jaros
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
| | - Vaclav Snasel
- Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia.
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Australia.
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland; School of Computing and Mathematical Sciences, University of Greenwich, London, UK.
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czechia; Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Poland.
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4
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Cuadros J, Z-Rivera L, Castro C, Whitaker G, Otero M, Weinstein A, Martínez-Montes E, Prado P, Zañartu M. DIVA Meets EEG: Model Validation Using Formant-Shift Reflex. APPLIED SCIENCES (BASEL, SWITZERLAND) 2023; 13:7512. [PMID: 38435340 PMCID: PMC10906992 DOI: 10.3390/app13137512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
The neurocomputational model 'Directions into Velocities of Articulators' (DIVA) was developed to account for various aspects of normal and disordered speech production and acquisition. The neural substrates of DIVA were established through functional magnetic resonance imaging (fMRI), providing physiological validation of the model. This study introduces DIVA_EEG an extension of DIVA that utilizes electroencephalography (EEG) to leverage the high temporal resolution and broad availability of EEG over fMRI. For the development of DIVA_EEG, EEG-like signals were derived from original equations describing the activity of the different DIVA maps. Synthetic EEG associated with the utterance of syllables was generated when both unperturbed and perturbed auditory feedback (first formant perturbations) were simulated. The cortical activation maps derived from synthetic EEG closely resembled those of the original DIVA model. To validate DIVA_EEG, the EEG of individuals with typical voices (N = 30) was acquired during an altered auditory feedback paradigm. The resulting empirical brain activity maps significantly overlapped with those predicted by DIVA_EEG. In conjunction with other recent model extensions, DIVA_EEG lays the foundations for constructing a complete neurocomputational framework to tackle vocal and speech disorders, which can guide model-driven personalized interventions.
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Affiliation(s)
- Jhosmary Cuadros
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del Táchira, San Cristóbal 5001, Venezuela
| | - Lucía Z-Rivera
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Escuela de Ingeniería Civil Biomédica, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2350026, Chile
| | - Christian Castro
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Escuela de Ingeniería Civil Biomédica, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2350026, Chile
| | - Grace Whitaker
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
| | - Mónica Otero
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago 8420524, Chile
- Centro Basal Ciencia & Vida, Universidad San Sebastián, Santiago 8580000, Chile
| | - Alejandro Weinstein
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Escuela de Ingeniería Civil Biomédica, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2350026, Chile
| | | | - Pavel Prado
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago 7510602, Chile
| | - Matías Zañartu
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
- Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
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5
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Shin H, Suma D, He B. Closed-loop motor imagery EEG simulation for brain-computer interfaces. Front Hum Neurosci 2022; 16:951591. [PMID: 36061506 PMCID: PMC9428352 DOI: 10.3389/fnhum.2022.951591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
In a brain-computer interface (BCI) system, the testing of decoding algorithms, tasks, and their parameters is critical for optimizing performance. However, conducting human experiments can be costly and time-consuming, especially when investigating broad sets of parameters. Attempts to utilize previously collected data in offline analysis lack a co-adaptive feedback loop between the system and the user present online, limiting the applicability of the conclusions obtained to real-world uses of BCI. As such, a number of studies have attempted to address this cost-wise middle ground between offline and live experimentation with real-time neural activity simulators. We present one such system which generates motor imagery electroencephalography (EEG) via forward modeling and novel motor intention encoding models for conducting sensorimotor rhythm (SMR)-based continuous cursor control experiments in a closed-loop setting. We use the proposed simulator with 10 healthy human subjects to test the effect of three decoder and task parameters across 10 different values. Our simulated approach produces similar statistical conclusions to those produced during parallel, paired, online experimentation, but in 55% of the time. Notably, both online and simulated experimentation expressed a positive effect of cursor velocity limit on performance regardless of subject average performance, supporting the idea of relaxing constraints on cursor gain in online continuous cursor control. We demonstrate the merits of our closed-loop motor imagery EEG simulation, and provide an open-source framework to the community for closed-loop SMR-based BCI studies in the future. All code including the simulator have been made available on GitHub.
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Kupers ER, Benson NC, Winawer J. A visual encoding model links magnetoencephalography signals to neural synchrony in human cortex. Neuroimage 2021; 245:118655. [PMID: 34687857 PMCID: PMC8788390 DOI: 10.1016/j.neuroimage.2021.118655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 10/11/2021] [Indexed: 01/23/2023] Open
Abstract
Synchronization of neuronal responses over large distances is hypothesized to be important for many cortical functions. However, no straightforward methods exist to estimate synchrony non-invasively in the living human brain. MEG and EEG measure the whole brain, but the sensors pool over large, overlapping cortical regions, obscuring the underlying neural synchrony. Here, we developed a model from stimulus to cortex to MEG sensors to disentangle neural synchrony from spatial pooling of the instrument. We find that synchrony across cortex has a surprisingly large and systematic effect on predicted MEG spatial topography. We then conducted visual MEG experiments and separated responses into stimulus-locked and broadband components. The stimulus-locked topography was similar to model predictions assuming synchronous neural sources, whereas the broadband topography was similar to model predictions assuming asynchronous sources. We infer that visual stimulation elicits two distinct types of neural responses, one highly synchronous and one largely asynchronous across cortex.
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Affiliation(s)
- Eline R Kupers
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States; Department of Psychology, Stanford University, Stanford, CA 94305, United States.
| | - Noah C Benson
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States; eSciences Institute, University of Washington, Seattle, WA 98195, United States
| | - Jonathan Winawer
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States
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7
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Cohen MX. A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiology. Neuroimage 2021; 247:118809. [PMID: 34906717 DOI: 10.1016/j.neuroimage.2021.118809] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 11/20/2021] [Accepted: 12/10/2021] [Indexed: 10/19/2022] Open
Abstract
The goal of this paper is to present a theoretical and practical introduction to generalized eigendecomposition (GED), which is a robust and flexible framework used for dimension reduction and source separation in multichannel signal processing. In cognitive electrophysiology, GED is used to create spatial filters that maximize a researcher-specified contrast. For example, one may wish to exploit an assumption that different sources have different frequency content, or that sources vary in magnitude across experimental conditions. GED is fast and easy to compute, performs well in simulated and real data, and is easily adaptable to a variety of specific research goals. This paper introduces GED in a way that ties together myriad individual publications and applications of GED in electrophysiology, and provides sample MATLAB and Python code that can be tested and adapted. Practical considerations and issues that often arise in applications are discussed.
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Affiliation(s)
- Michael X Cohen
- Donders Centre for Medical Neuroscience, Radboud University Medical Center, the Netherlands.
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8
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Dimitrov T, He M, Stickgold R, Prerau MJ. Sleep spindles comprise a subset of a broader class of electroencephalogram events. Sleep 2021; 44:zsab099. [PMID: 33857311 PMCID: PMC8436142 DOI: 10.1093/sleep/zsab099] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/05/2021] [Indexed: 12/15/2022] Open
Abstract
STUDY OBJECTIVES Sleep spindles are defined based on expert observations of waveform features in the electroencephalogram (EEG) traces. This is a potentially limiting characterization, as transient oscillatory bursts like spindles are easily obscured in the time domain by higher amplitude activity at other frequencies or by noise. It is therefore highly plausible that many relevant events are missed by current approaches based on traditionally defined spindles. Given their oscillatory structure, we reexamine spindle activity from first principles, using time-frequency activity in comparison to scored spindles. METHODS Using multitaper spectral analysis, we observe clear time-frequency peaks in the sigma (10-16 Hz) range (TFσ peaks). While nearly every scored spindle coincides with a TFσ peak, numerous similar TFσ peaks remain undetected. We therefore perform statistical analyses of spindles and TFσ peaks using manual and automated detection methods, comparing event cooccurrence, morphological similarities, and night-to-night consistency across multiple datasets. RESULTS On average, TFσ peaks have more than three times the rate of spindles (mean rate: 9.8 vs. 3.1 events/minute). Moreover, spindles subsample the most prominent TFσ peaks with otherwise identical spectral morphology. We further demonstrate that detected TFσ peaks have stronger night-to-night rate stability (ρ = 0.98) than spindles (ρ = 0.67), while covarying with spindle rates across subjects (ρ = 0.72). CONCLUSIONS These results provide compelling evidence that traditionally defined spindles constitute a subset of a more generalized class of EEG events. TFσ peaks are therefore a more complete representation of the underlying phenomenon, providing a more consistent and robust basis for future experiments and analyses.
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Affiliation(s)
- Tanya Dimitrov
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital Department of Medicine, Boston, MA
| | - Mingjian He
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital Department of Medicine, Boston, MA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Robert Stickgold
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA
- Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Michael J Prerau
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital Department of Medicine, Boston, MA
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9
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He M, Liu F, Nummenmaa A, Hämäläinen M, Dickerson BC, Purdon PL. Age-Related EEG Power Reductions Cannot Be Explained by Changes of the Conductivity Distribution in the Head Due to Brain Atrophy. Front Aging Neurosci 2021; 13:632310. [PMID: 33679380 PMCID: PMC7929986 DOI: 10.3389/fnagi.2021.632310] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 01/18/2021] [Indexed: 11/28/2022] Open
Abstract
Electroencephalogram (EEG) power reductions in the aging brain have been described by numerous previous studies. However, the underlying mechanism for the observed brain signal power reduction remains unclear. One possible cause for reduced EEG signals in elderly subjects might be the increased distance from the primary neural electrical currents on the cortex to the scalp electrodes as the result of cortical atrophies. While brain shrinkage itself reflects age-related neurological changes, the effects of changes in the distribution of electrical conductivity are often not distinguished from altered neural activity when interpreting EEG power reductions. To address this ambiguity, we employed EEG forward models to investigate whether brain shrinkage is a major factor for the signal attenuation in the aging brain. We simulated brain shrinkage in spherical and realistic brain models and found that changes in the conductor geometry cannot fully account for the EEG power reductions even when the brain was shrunk to unrealistic sizes. Our results quantify the extent of power reductions from brain shrinkage and pave the way for more accurate inferences about deficient neural activity and circuit integrity based on EEG power reductions in the aging population.
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Affiliation(s)
- Mingjian He
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology (MIT), Cambridge, MA, United States
| | - Feng Liu
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Matti Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Bradford C Dickerson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States.,Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States
| | - Patrick L Purdon
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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Barry RJ, De Blasio FM. Characterizing pink and white noise in the human electroencephalogram. J Neural Eng 2021; 18. [PMID: 33545698 DOI: 10.1088/1741-2552/abe399] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 02/05/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The power spectrum of the human electroencephalogram (EEG) as a function of frequency is a mix of brain oscillations (e.g. alpha activity around 10 Hz) and non-oscillations or noise of uncertain origin. "White noise" is uniformly distributed over frequency, while "pink noise" has an inverse power-frequency relation (power ∝ 1/f). Interest in EEG pink noise has been growing, but previous human estimates appear methodologically flawed. We propose a new approach to extract separate valid estimates of pink and white noise from an EEG power spectrum. APPROACH We use simulated data to demonstrate its effectiveness compared with established procedures, and provide an illustrative example from a new resting eyes-open (EO) and eyes-closed (EC) dataset. The topographic characteristics of the obtained pink and white noise estimates are examined, as is the alpha power in this sample. MAIN RESULTS Valid pink and white noise estimates were successfully obtained for each of our 5400 individual spectra (60 participants × 30 electrodes × 3 conditions/blocks [EO1, EC, EO2]). The 1/f noise had a distinct central scalp topography, and white noise was occipital in distribution, both differing from the parietal topography of the alpha oscillation. These differences point to their separate neural origins. EC pink and white noise powers were globally greater than in EO. SIGNIFICANCE This valid estimation of pink and white noise in the human EEG holds promise for more accurate assessment of oscillatory neural activity in both typical and clinical groups, such as those with attention deficits. Further, outside the human EEG, the new methodology can be generalized to remove noise from spectra in many fields of science and technology.
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Affiliation(s)
- Robert J Barry
- School of Psychology, University of Wollongong, Northfields Ave, Wollongong, Wollongong, New South Wales, 2522, AUSTRALIA
| | - Frances M De Blasio
- School of Psychology, University of Wollongong, Northfields Ave, Wollongong, New South Wales, 2522, AUSTRALIA
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11
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Schrödinger filtering: a precise EEG despiking technique for EEG-fMRI gradient artifact. Neuroimage 2020; 226:117525. [PMID: 33246129 DOI: 10.1016/j.neuroimage.2020.117525] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 09/22/2020] [Accepted: 10/27/2020] [Indexed: 11/20/2022] Open
Abstract
In EEG data acquired in the presence of fMRI, gradient-related spike artifacts contaminate the signal following the common preprocessing step of average artifact subtraction. Spike artifacts compromise EEG data quality since they overlap with the EEG signal in frequency, thereby confounding frequency-based inferences on activity. As well, spike artifacts can inflate or deflate correlations among time series, thereby confounding inferences on functional connectivity. We present Schrödinger filtering, which uses the Schrödinger equation to decompose the spike-containing input. The basis functions of the decomposition are localized and pulse-shaped, and selectively capture the various input peaks, with the spike components clustered at the beginning of the spectrum. Schrödinger filtering automatically subtracts the spike components from the data. On real and simulated data, we show that Schrödinger filtering (1) simultaneously accomplishes high spike removal and high signal preservation without affecting evoked activity, and (2) reduces spurious pairwise correlations in spontaneous activity. In these regards, Schrödinger filtering was significantly better than three other despiking techniques: median filtering, amplitude thresholding, and wavelet denoising. These results encourage the use of Schrödinger filtering in future EEG-fMRI pipelines, as well as in other spike-related applications (e.g., fMRI motion artifact removal or action potential extraction).
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