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Samadzadehaghdam, Nasser, Makkiabadi, Bahador, Eqlimi, Ehsan, Mohagheghian, Fahimeh, Khajehpoor, Hassan, Harirchian, Mohammad Hossein. (1400). Developing a Multi-channel Beamformer by Enhancing Spatially Constrained ICA for Recovery of Correlated EEG Sources. سامانه مدیریت نشریات علمی, 11(2), 205-214. doi: 10.31661/jbpe.v0i0.801
Nasser Samadzadehaghdam; Bahador Makkiabadi; Ehsan Eqlimi; Fahimeh Mohagheghian; Hassan Khajehpoor; Mohammad Hossein Harirchian. "Developing a Multi-channel Beamformer by Enhancing Spatially Constrained ICA for Recovery of Correlated EEG Sources". سامانه مدیریت نشریات علمی, 11, 2, 1400, 205-214. doi: 10.31661/jbpe.v0i0.801
Samadzadehaghdam, Nasser, Makkiabadi, Bahador, Eqlimi, Ehsan, Mohagheghian, Fahimeh, Khajehpoor, Hassan, Harirchian, Mohammad Hossein. (1400). 'Developing a Multi-channel Beamformer by Enhancing Spatially Constrained ICA for Recovery of Correlated EEG Sources', سامانه مدیریت نشریات علمی, 11(2), pp. 205-214. doi: 10.31661/jbpe.v0i0.801
Samadzadehaghdam, Nasser, Makkiabadi, Bahador, Eqlimi, Ehsan, Mohagheghian, Fahimeh, Khajehpoor, Hassan, Harirchian, Mohammad Hossein. Developing a Multi-channel Beamformer by Enhancing Spatially Constrained ICA for Recovery of Correlated EEG Sources. سامانه مدیریت نشریات علمی, 1400; 11(2): 205-214. doi: 10.31661/jbpe.v0i0.801

Developing a Multi-channel Beamformer by Enhancing Spatially Constrained ICA for Recovery of Correlated EEG Sources

Journal of Biomedical Physics and Engineering
مقاله 10، دوره 11، شماره 2، خرداد و تیر 2021، صفحه 205-214    اصل مقاله (962.12 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.801
نویسندگان
Nasser Samadzadehaghdam1، 2؛ Bahador Makkiabadi* 1، 3؛ Ehsan Eqlimi4، 5؛ Fahimeh Mohagheghian6؛ Hassan Khajehpoor1، 3؛ Mohammad Hossein Harirchian7
1PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
2PhD, Department of Medical Bioengineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
3PhD, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
4PhD Candidate, Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran
5PhD Candidate, Research Center for Biomedical Technology and Robotics (RCBTR), Institute of Advanced Medical Technologies (IAMT), Tehran University of Medical Sciences (TUMS), Tehran, Iran
6PhD, Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
7MD, Iranian Centre of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
چکیده
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.
کلیدواژه‌ها
ICA Based Beamformer؛ Correlated Sources Recovery؛ Signal Processing, Computer-Assisted؛ Electroencephalography؛ Brain Waves
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