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Pope KJ, Lewis TW, Fitzgibbon SP, Janani AS, Grummett TS, Williams PAH, Battersby M, Bastiampillai T, Whitham EM, Willoughby JO. Managing electromyogram contamination in scalp recordings: An approach identifying reliable beta and gamma EEG features of psychoses or other disorders. Brain Behav 2022; 12:e2721. [PMID: 35919931 PMCID: PMC9480942 DOI: 10.1002/brb3.2721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 06/05/2022] [Accepted: 07/07/2022] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE In publications on the electroencephalographic (EEG) features of psychoses and other disorders, various methods are utilized to diminish electromyogram (EMG) contamination. The extent of residual EMG contamination using these methods has not been recognized. Here, we seek to emphasize the extent of residual EMG contamination of EEG. METHODS We compared scalp electrical recordings after applying different EMG-pruning methods with recordings of EMG-free data from 6 fully paralyzed healthy subjects. We calculated the ratio of the power of pruned, normal scalp electrical recordings in the six subjects, to the power of unpruned recordings in the same subjects when paralyzed. We produced "contamination graphs" for different pruning methods. RESULTS EMG contamination exceeds EEG signals progressively more as frequencies exceed 25 Hz and with distance from the vertex. In contrast, Laplacian signals are spared in central scalp areas, even to 100 Hz. CONCLUSION Given probable EMG contamination of EEG in psychiatric and other studies, few findings on beta- or gamma-frequency power can be relied upon. Based on the effectiveness of current methods of EEG de-contamination, investigators should be able to reanalyze recorded data, reevaluate conclusions from high-frequency EEG data, and be aware of limitations of the methods.
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Affiliation(s)
- Kenneth J Pope
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia.,Medical Device Research Institute, Flinders University, Adelaide, South Australia, Australia
| | - Trent W Lewis
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia.,Medical Device Research Institute, Flinders University, Adelaide, South Australia, Australia
| | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Azin S Janani
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia.,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
| | - Tyler S Grummett
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia.,Medical Device Research Institute, Flinders University, Adelaide, South Australia, Australia.,Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia
| | - Patricia A H Williams
- College of Science and Engineering, Flinders University, Adelaide, South Australia, Australia.,Flinders Digital Health Research Centre, Flinders University, Adelaide, South Australia, Australia
| | - Malcolm Battersby
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia.,Department of Psychiatry, Flinders Medical Centre, Adelaide, South Australia, Australia
| | - Tarun Bastiampillai
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia.,Department of Psychiatry, Flinders Medical Centre, Adelaide, South Australia, Australia
| | - Emma M Whitham
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia.,Department of Neurology, Flinders Medical Centre, Adelaide, South Australia, Australia
| | - John O Willoughby
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia.,Department of Neurology, Flinders Medical Centre, Adelaide, South Australia, Australia
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Ojeda A, Kreutz-Delgado K, Mishra J. Bridging M/EEG Source Imaging and Independent Component Analysis Frameworks Using Biologically Inspired Sparsity Priors. Neural Comput 2021; 33:2408-2438. [PMID: 34412115 DOI: 10.1162/neco_a_01415] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 03/24/2021] [Indexed: 11/04/2022]
Abstract
Electromagnetic source imaging (ESI) and independent component analysis (ICA) are two popular and apparently dissimilar frameworks for M/EEG analysis. This letter shows that the two frameworks can be linked by choosing biologically inspired source sparsity priors. We demonstrate that ESI carried out by the sparse Bayesian learning (SBL) algorithm yields source configurations composed of a few active regions that are also maximally independent from one another. In addition, we extend the standard SBL approach to source imaging in two important directions. First, we augment the generative model of M/EEG to include artifactual sources. Second, we modify SBL to allow for efficient model inversion with sequential data. We refer to this new algorithm as recursive SBL (RSBL), a source estimation filter with potential for online and offline imaging applications. We use simulated data to verify that RSBL can accurately estimate and demix cortical and artifactual sources under different noise conditions. Finally, we show that on real error-related EEG data, RSBL can yield single-trial source estimates in agreement with the experimental literature. Overall, by demonstrating that ESI can produce maximally independent sources while simultaneously localizing them in cortical space, we bridge the gap between the ESI and ICA frameworks for M/EEG analysis.
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Affiliation(s)
- Alejandro Ojeda
- Neural Engineering and Translation Labs, Department of Psychiatry, and Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093 U.S.A. alejo.ojeda83@gmail dot com
| | - Kenneth Kreutz-Delgado
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093 U.S.A.
| | - Jyoti Mishra
- Neural Engineering and Translation Labs, Department of Psychiatry, University of California San Diego, CA 92093, U.S.A.
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Tremmel C, Herff C, Sato T, Rechowicz K, Yamani Y, Krusienski DJ. Estimating Cognitive Workload in an Interactive Virtual Reality Environment Using EEG. Front Hum Neurosci 2019; 13:401. [PMID: 31803035 PMCID: PMC6868478 DOI: 10.3389/fnhum.2019.00401] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 10/25/2019] [Indexed: 01/05/2023] Open
Abstract
With the recent surge of affordable, high-performance virtual reality (VR) headsets, there is unlimited potential for applications ranging from education, to training, to entertainment, to fitness and beyond. As these interfaces continue to evolve, passive user-state monitoring can play a key role in expanding the immersive VR experience, and tracking activity for user well-being. By recording physiological signals such as the electroencephalogram (EEG) during use of a VR device, the user's interactions in the virtual environment could be adapted in real-time based on the user's cognitive state. Current VR headsets provide a logical, convenient, and unobtrusive framework for mounting EEG sensors. The present study evaluates the feasibility of passively monitoring cognitive workload via EEG while performing a classical n-back task in an interactive VR environment. Data were collected from 15 participants and the spatio-spectral EEG features were analyzed with respect to task performance. The results indicate that scalp measurements of electrical activity can effectively discriminate three workload levels, even after suppression of a co-varying high-frequency activity.
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Affiliation(s)
- Christoph Tremmel
- Biomedical Engineering, Old Dominion University, Norfolk, VA, United States
| | - Christian Herff
- Department of Neurosurgery, School of Mental Health and Neurosciences, Maastricht University, Maastricht, Netherlands
| | - Tetsuya Sato
- Department of Psychology, Old Dominion University, Norfolk, VA, United States
| | - Krzysztof Rechowicz
- Virginia Modeling, Analysis and Simulation Center (VMASC), Suffolk, VA, United States
| | - Yusuke Yamani
- Department of Psychology, Old Dominion University, Norfolk, VA, United States
| | - Dean J. Krusienski
- Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA, United States
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Janani AS, Grummett TS, Lewis TW, Fitzgibbon SP, Whitham EM, DelosAngeles D, Bakhshayesh H, Willoughby JO, Pope KJ. Improved artefact removal from EEG using Canonical Correlation Analysis and spectral slope. J Neurosci Methods 2018; 298:1-15. [PMID: 29408174 DOI: 10.1016/j.jneumeth.2018.01.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Revised: 01/19/2018] [Accepted: 01/19/2018] [Indexed: 11/27/2022]
Abstract
BACKGROUND Contamination of scalp measurement by tonic muscle artefacts, even in resting positions, is an unavoidable issue in EEG recording. These artefacts add significant energy to the recorded signals, particularly at high frequencies. To enable reliable interpretation of subcortical brain activity, it is necessary to detect and discard this contamination. NEW METHOD We introduce a new automatic muscle-removal approach based on the traditional Blind Source Separation-Canonical Correlation Analysis (BSS-CCA) method and the spectral slope of its components. We show that CCA-based muscle-removal methods can discriminate between signals with high correlation coefficients (brain, mains artefact) and signals with low correlation coefficients (white noise, muscle). We also show that typical BSS-CCA components are not purely from one source, but are mixtures from multiple sources, limiting the performance of BSS-CCA in artefact removal. We demonstrate, using our paralysis dataset, improved performance using BSS-CCA followed by spectral-slope rejection. RESULT This muscle removal approach can reduce high-frequency muscle contamination of EEG, especially at peripheral channels, while preserving steady-state brain responses in cognitive tasks. COMPARISON WITH EXISTING METHODS This approach is automatic and can be applied on any sample of data easily. The results show its performance is comparable with the ICA method in removing muscle contamination and has significantly lower computational complexity. CONCLUSION We identify limitations of the traditional BSS-CCA approach to artefact removal in EEG, propose and test an extension based on spectral slope that makes it automatic and improves its performance, and results in performance comparable to competitors such as ICA-based artefact removal.
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Affiliation(s)
- Azin S Janani
- College of Science and Engineering, Flinders University, Adelaide, Australia; Medical Device Research Institute, Flinders University, Adelaide, Australia.
| | - Tyler S Grummett
- College of Science and Engineering, Flinders University, Adelaide, Australia; Medical Device Research Institute, Flinders University, Adelaide, Australia; Centre for Neuroscience, Flinders University, Adelaide, Australia
| | - Trent W Lewis
- College of Science and Engineering, Flinders University, Adelaide, Australia; Medical Device Research Institute, Flinders University, Adelaide, Australia
| | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Emma M Whitham
- Department of Neurology, Flinders Medical Centre, Adelaide, Australia
| | - Dylan DelosAngeles
- College of Science and Engineering, Flinders University, Adelaide, Australia; Centre for Neuroscience, Flinders University, Adelaide, Australia
| | - Hanieh Bakhshayesh
- College of Science and Engineering, Flinders University, Adelaide, Australia; Medical Device Research Institute, Flinders University, Adelaide, Australia
| | - John O Willoughby
- Centre for Neuroscience, Flinders University, Adelaide, Australia; College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Kenneth J Pope
- College of Science and Engineering, Flinders University, Adelaide, Australia; Medical Device Research Institute, Flinders University, Adelaide, Australia
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