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Samadzadehaghdam N, MakkiAbadi B, Eqlimi E, Mohagheghian F, Khajehpoor H, Harirchian MH. Developing a Multi-channel Beamformer by Enhancing Spatially Constrained ICA for Recovery of Correlated EEG Sources. J Biomed Phys Eng 2021; 11:205-214. [PMID: 33937127 PMCID: PMC8064133 DOI: 10.31661/jbpe.v0i0.801] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2017] [Accepted: 10/14/2017] [Indexed: 11/23/2022]
Abstract
Background: Brain source imaging based on electroencephalogram (EEG) data aims to recover the neuron populations’ activity producing the scalp potentials. This procedure is known as the EEG inverse problem. Recently, beamformers have gained a lot of consideration in the EEG inverse problem. Objective: Beamformers lack acceptable performance in the case of correlated brain sources. These sources happen when some regions of the brain have simultaneous or correlated activities such as auditory stimulation or moving left and right extremities of the body at the same time. In this paper, we have developed a multichannel beamformer robust to correlated sources. Material and Methods: In this simulation study, we have looked at the problem of brain source imaging and beamforming from a blind source separation point of view. We focused on the spatially constraint independent component analysis (scICA) algorithm, which generally benefits from the pre-known partial information of mixing matrix, and modified the steps of the algorithm in a way that makes it more robust to correlated sources. We called the modified scICA algorithm Multichannel ICA based EEG Beamformer (MIEB). Results: We evaluated the proposed algorithm on simulated EEG data and compared its performance quantitatively with three algorithms scICA, linearly-constrained minimum-variance (LCMV) and Dual-Core beamformers; it is considered that the latter is specially designed to reconstruct correlated sources. Conclusion: The MIEB algorithm has much better performance in terms of normalized mean squared error in recovering the correlated/uncorrelated sources both in noise free and noisy synthetic EEG signals. Therefore, it could be used as a robust beamformer in recovering correlated brain sources.
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Affiliation(s)
- Nasser Samadzadehaghdam
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- PhD, Department of Medical Bioengineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Bahador MakkiAbadi
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- PhD, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Ehsan Eqlimi
- PhD Candidate, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- PhD Candidate, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Fahimeh Mohagheghian
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hassan Khajehpoor
- PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- PhD, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mohammad Hossein Harirchian
- MD, Iranian Centre of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
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Mohagheghian F, Makkiabadi B, Jalilvand H, Khajehpoor H, Samadzadehaghdam N, Eqlimi E, Deevband MR. Computer-Aided Tinnitus Detection based on Brain Network Analysis of EEG Functional Connectivity. J Biomed Phys Eng 2020; 9:687-698. [PMID: 32039100 PMCID: PMC6943854 DOI: 10.31661/jbpe.v0i0.937] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2018] [Accepted: 06/30/2018] [Indexed: 01/04/2023]
Abstract
Background Tinnitus known as a central nervous system disorder is correlated with specific oscillatory activities within auditory and non-auditory brain areas. Several studies in the past few years have revealed that in the most tinnitus cases, the response pattern of neurons in auditory system is changed due to auditory deafferentation, which leads to variation and disruption of the brain networks. Objective In this paper, we introduce an approach to automatically distinguish tinnitus individuals from healthy controls based on whole-brain functional connectivity and network analysis. Material and Methods The functional connectivity analysis was applied to the resting state electroencephalographic (EEG) data of both groups using Weighted Phase Lag Index (WPLI) for various frequency bands in 2-44 Hz frequency range. In this case- control study, the classification was performed on graph theoretical measures using support vector machine (SVM) as a robust classification method. Results Experimental results showed promising classification performance with a high accuracy, sensitivity, and specificity in all frequency bands, specifically in the beta2 frequency band. Conclusion The current study provides substantial evidence that tinnitus network can be successfully detected by consistent measures of the brain networks based on EEG functional connectivity.
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Affiliation(s)
- F Mohagheghian
- PhD, Department of Medical Physics and Biomedical engineering, School of Medicine, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
| | - B Makkiabadi
- PhD, Department of Medical Physics and Biomedical engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- PhD, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - H Jalilvand
- PhD, Department of Audiology, School of Rehabilitation, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
| | - H Khajehpoor
- MSc, Department of Medical Physics and Biomedical engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- MSc, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - N Samadzadehaghdam
- MSc, Department of Medical Physics and Biomedical engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- MSc, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - E Eqlimi
- MSc, Department of Medical Physics and Biomedical engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
- MSc, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - M R Deevband
- PhD, Department of Medical Physics and Biomedical engineering, School of Medicine, Shahid Beheshti University of Medical Sciences (SBMU), Tehran, Iran
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