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Wang R, Fu K, Zhao R, Wang D, Yang Z, Bin W, Gao Y, Ning X. Expanding the clinical application of OPM-MEG using an effective automatic suppression method for the dental brace metal artifact. Neuroimage 2024; 296:120661. [PMID: 38838840 DOI: 10.1016/j.neuroimage.2024.120661] [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: 01/01/2024] [Revised: 05/19/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024] Open
Abstract
Optically pumped magnetometer magnetoencephalography (OPM-MEG) holds significant promise for clinical functional brain imaging due to its superior spatiotemporal resolution. However, effectively suppressing metallic artifacts, particularly from devices such as orthodontic braces and vagal nerve stimulators remains a major challenge, hindering the wider clinical application of wearable OPM-MEG devices. A comprehensive analysis of metal artifact characteristics from time, frequency, and time-frequency perspectives was conducted for the first time using an OPM-MEG device in clinical medicine. This study focused on patients with metal orthodontics, examining the modulation of metal artifacts by breath and head movement, the incomplete regular sub-Gaussian distribution, and the high absolute power ratio in the 0.5-8 Hz band. The existing metal artifact suppression algorithms applied to SQUID-MEG, such as fast independent component analysis (FastICA), information maximization (Infomax), and algorithms for multiple unknown signal extraction (AMUSE), exhibit limited efficacy. Consequently, this study introduced the second-order blind identification (SOBI) algorithm, which utilized multiple time delays for the component separation of OPM-MEG measurement signals. We modified the time delays of the SOBI method to improve its efficacy in separating artifact components, particularly those in the ultralow frequency range. This approach employs the frequency-domain absolute power ratio, root mean square (RMS) value, and mutual information methods to automate the artifact component screening process. The effectiveness of this method was validated through simulation experiments involving four subjects in both resting and evoked experiments. In addition, the proposed method was also validated by the actual OPM-MEG evoked experiments of three subjects. Comparative analyses were conducted against the FastICA, Infomax, and AMUSE algorithms. Evaluation metrics included normalized mean square error, normalized delta band power error, RMS error, and signal-to-noise ratio, demonstrating that the proposed method provides optimal suppression of metal artifacts. This advancement holds promise for enhancing data quality and expanding the clinical applications of OPM-MEG.
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Affiliation(s)
- Ruonan Wang
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou 310051, China.
| | - Kaiwen Fu
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou 310051, China.
| | - Ruochen Zhao
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou 310051, China.
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan 250012, China; National Innovation Platform for industry-Education Integration in Medicine-Engineering Interdisciplinary, Shandong Key Laboratory for Magnetic Field-free Medicine and Functional Imaging, Shandong University, Research Institute of Shandong University, Jinan, 250014, China.
| | - Zhimin Yang
- State Key Laboratory of Traditional Chinese Medicine Syndrome/Health Construction Center, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.
| | - Wei Bin
- State Key Laboratory of Traditional Chinese Medicine Syndrome/Health Construction Center, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.
| | - Yang Gao
- Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Hangzhou 310051, China; National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou 310051, China.
| | - Xiaolin Ning
- School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China; National Innovation Platform for industry-Education Integration in Medicine-Engineering Interdisciplinary, Shandong Key Laboratory for Magnetic Field-free Medicine and Functional Imaging, Shandong University, Research Institute of Shandong University, Jinan, 250014, China; National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou 310051, China.
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Dien J. Multi-Algorithm Artifact Correction (MAAC) procedure part one: Algorithm and example. Biol Psychol 2024; 188:108775. [PMID: 38499226 DOI: 10.1016/j.biopsycho.2024.108775] [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: 03/01/2021] [Revised: 03/10/2024] [Accepted: 03/14/2024] [Indexed: 03/20/2024]
Abstract
The Multi-Algorithm Artifact Correction (MAAC) procedure is presented for electroencephalographic (EEG) data, as made freely available in the open-source EP Toolkit (Dien, 2010). First the major EEG artifact correction methods (regression, spatial filters, principal components analysis, and independent components analysis) are reviewed. Contrary to the dominant approach of picking one method that is thought to be most effective, this review concludes that none are globally superior, but rather each has strengths and weaknesses. Then each of the major artifact types are reviewed (Blink, Corneo-Retinal Dipole, Saccadic Spike Potential, and Movement). For each one, it is proposed that one of the major correction methods is best matched to address it, resulting in the MAAC procedure. The MAAC itself is then presented, as implemented in the EP Toolkit, in order to provide a sense of the user experience. The primary goal of this present paper is to make the conceptual argument for the MAAC approach.
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Affiliation(s)
- Joseph Dien
- Department of Human Development and Quantitative Methodology, University of Maryland, 3304 Benjamin Building, College Park, MD 20742, USA.
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Sun R, Cheng ASK, Chan C, Hsiao J, Privitera AJ, Gao J, Fong C, Ding R, Tang AC. Tracking gaze position from EEG: Exploring the possibility of an EEG-based virtual eye-tracker. Brain Behav 2023; 13:e3205. [PMID: 37721530 PMCID: PMC10570499 DOI: 10.1002/brb3.3205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 07/18/2023] [Accepted: 07/22/2023] [Indexed: 09/19/2023] Open
Abstract
INTRODUCTION Ocular artifact has long been viewed as an impediment to the interpretation of electroencephalogram (EEG) signals in basic and applied research. Today, the use of blind source separation (BSS) methods, including independent component analysis (ICA) and second-order blind identification (SOBI), is considered an essential step in improving the quality of neural signals. Recently, we introduced a method consisting of SOBI and a discriminant and similarity (DANS)-based identification method, capable of identifying and extracting eye movement-related components. These recovered components can be localized within ocular structures with a high goodness of fit (>95%). This raised the possibility that such EEG-derived SOBI components may be used to build predictive models for tracking gaze position. METHODS As proof of this new concept, we designed an EEG-based virtual eye-tracker (EEG-VET) for tracking eye movement from EEG alone. The EEG-VET is composed of a SOBI algorithm for separating EEG signals into different components, a DANS algorithm for automatically identifying ocular components, and a linear model to transfer ocular components into gaze positions. RESULTS The prototype of EEG-VET achieved an accuracy of 0.920° and precision of 1.510° of a visual angle in the best participant, whereas an average accuracy of 1.008° ± 0.357° and a precision of 2.348° ± 0.580° of a visual angle across all participants (N = 18). CONCLUSION This work offers a novel approach that readily co-registers eye movement and neural signals from a single-EEG recording, thus increasing the ease of studying neural mechanisms underlying natural cognition in the context of free eye movement.
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Affiliation(s)
- Rui Sun
- Department of Rehabilitation SciencesThe Hong Kong Polytechnic UniversityHong Kong SARChina
- The Laboratory of Neuroscience for EducationThe University of Hong KongHong Kong SARChina
| | - Andy S. K. Cheng
- Department of Rehabilitation SciencesThe Hong Kong Polytechnic UniversityHong Kong SARChina
| | - Cynthia Chan
- Department of PsychologyThe University of Hong KongHong Kong SARChina
| | - Janet Hsiao
- Department of PsychologyThe University of Hong KongHong Kong SARChina
| | - Adam J. Privitera
- Centre for Research and Development in LearningNanyang Technological UniversitySingapore
| | - Junling Gao
- Centre of Buddhism StudiesThe University of Hong KongHong Kong SARChina
| | - Ching‐hang Fong
- Department of Rehabilitation SciencesThe Hong Kong Polytechnic UniversityHong Kong SARChina
| | - Ruoxi Ding
- China Center for Health Development StudiesPeking UniversityBeijingChina
| | - Akaysha C. Tang
- The Laboratory of Neuroscience for EducationThe University of Hong KongHong Kong SARChina
- Neural DialogueShenzhenChina
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Hu Q, Li M, Li Y. Single-channel EEG signal extraction based on DWT, CEEMDAN, and ICA method. Front Hum Neurosci 2022; 16:1010760. [PMID: 36211125 PMCID: PMC9532603 DOI: 10.3389/fnhum.2022.1010760] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 08/29/2022] [Indexed: 12/04/2022] Open
Abstract
In special application scenarios, such as portable anesthesia depth monitoring, portable emotional state recognition and portable sleep monitoring, electroencephalogram (EEG) signal acquisition equipment is required to be convenient and easy to use. It is difficult to remove electrooculogram (EOG) artifacts when the number of EEG acquisition channels is small, especially when the number of observed signals is less than that of the source signals, and the overcomplete problem will arise. The independent component analysis (ICA) algorithm commonly used for artifact removal requires the number of basis vectors to be smaller than the dimension of the input data due to a set of standard orthonormal bases learned during the convergence process, so it cannot be used to solve the overcomplete problem. The empirical mode decomposition method decomposes the signal into several independent intrinsic mode functions so that the number of observed signals is more than that of the source signals, solving the overcomplete problem. However, when using this method to solve overcompleteness, the modal aliasing problem will arise, which is caused by abnormal events such as sharp signals, impulse interference, and noise. Aiming at the above problems, we propose a novel EEG artifact removal method based on discrete wavelet transform, complete empirical mode decomposition for adaptive noise (CEEMDAN) and ICA in this paper. First, the input signals are transformed by discrete wavelet (DWT), and then CEEMDAN is used to solve the overcomplete and mode aliasing problems, meeting the a priori conditions of the ICA algorithm. Finally, the components belonging to EOG artifacts are removed according to the sample entropy value of each independent component. Experiments show that this method can effectively remove EOG artifacts while solving the overcomplete and modal aliasing problems.
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Affiliation(s)
- Qinghui Hu
- School of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin, China
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | - Mingxin Li
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | - Yunde Li
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, China
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Privitera AJ, Tang AC. Functional Significance of Individual Differences in P3 Network Spatial Configuration. J PSYCHOPHYSIOL 2022. [DOI: 10.1027/0269-8803/a000295] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abstract. The amplitude and latency of the P3 component in the electroencephalogram (EEG) event-related potentials (ERPs) are among the most extensively used markers for individual differences in normal and abnormal brain functions. In contrast, individual variations in spatial topography of the temporally-defined P3 component are relatively under-explored. Development in EEG-based source imaging opened up the possibility that individual-specific spatial configuration of the neural network underlying the temporally-defined P3 component bear a novel source of information for marking an individual difference in behavioral and cognitive function. In testing this hypothesis, a hybrid method consisting of blind source separation (BSS), equivalent current dipole (ECD) modeling, and hits-vector-based analysis was applied to continuous un-epoched EEG data collected from 13 healthy human participants performing a visual color oddball task. By analyzing the spatial configuration of the network underlying the temporally-defined P3 component, hereafter referred to as the P3N, we discovered that the contribution of each constituent structure within the P3N is not uniform. Instead, frontal lobe structures have significantly more involvement than other constituent structures, as quantitatively characterized by cross-individual reliability and a within-individual contribution to the P3N. A factor analysis of the hits vector data revealed that although P3 latency and amplitude did not show significant correlations with measures of the behavioral outcomes, scores of two factors derived from the hits vectors selectively predict behavioral reaction time and response correctness. These results support the hypothesis that variations in P3 spatial configuration reflect not merely noise but individual-specific features with functional significance.
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Affiliation(s)
- Adam John Privitera
- College of Liberal Arts, Wenzhou-Kean University, Wenzhou, PR China
- Faculty of Education, The University of Hong Kong, Hong Kong, SAR, PR China
| | - Akaysha C. Tang
- Neural Dialogue Shenzhen Educational Technology, Shenzhen, PR China
- Neuroscience for Education Group, The University of Hong Kong, Hong Kong, SAR, PR China
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