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Group-Based EEG Source Mapping of the Visual Cortex Using Adaptive MSP Localization

Authors

  • Anwar Ali Gaho

    Department of Electrical Engineering, Indus University, Karachi, Pakistan
    Author

  • Ahmed Muddassir Khan

    Department of Electrical Engineering, Indus University, Karachi, Pakistan
    Author

  • Anwer Ahmed Memon

    Department of Electrical Engineering, Indus University, Karachi, Pakistan
    Author
    https://orcid.org/0000-0001-6600-204X

  • Sayed Hyder Abbas Musavi

    Department of Electrical Engineering, Mehran Engineering College Khairpur, Pakistan
    Author

  • Muhammad Abul Hassan

    Department of Biomedical Engineering, NED University of Engineering & Technology, Karachi, Pakistan
    Author

  • Shehla Honey

    Department of Physics, University of Okara, Okara, Pakistan
    Author

DOI:

https://doi.org/10.22581/muet1982.0176

Keywords:

Electroencephalography , Multiple Sparse Priors , Bayesian Framework , Visual Cortex, Free Energy

Abstract

Approximately 10–20% of the global population (~8.2 billion in 2025) is affected by neurological disorders, including migraine (~12%), Alzheimer’s/dementia (~0.7%), epilepsy (~0.6%), and Parkinson’s disease (~0.1%). Electroencephalography (EEG) serves as a critical modality for capturing electrophysiological brain dynamics in such conditions. Advanced engineering techniques—including inverse problem regularization, spatial filtering, source reconstruction algorithms, and computational modeling—enhance the accuracy, spatial resolution, and clinical applicability of EEG systems. EEG source localization, supported by biomedical signal processing pipelines, plays a key role in diagnosing and treating 10–15% of brain disorders involving abnormal cortical excitability.

This research proposes a novel computational approach to EEG source localization that emphasizes engineering innovation in biomedical signal processing and system optimization. Specifically, it targets neural activity within the visual cortex, applying a Bayesian framework with Multiple Sparse Priors inversion. The proposed algorithm employs adaptive patch variation to optimize Free Energy and reduce localization errors across subject groups in response to facial stimuli, including familiar, unfamiliar, and scrambled faces. By addressing the ill-posed nature of the EEG source localization problem, this approach mitigates uncertainties caused by factors such as noise, neural overlaps, and structural defects. The results are validated using a synthetic EEG dataset. MATLAB-based SPM12 toolbox is used for pre-processing and statistical analysis.  In this study, EEG source localization was performed on six subjects (three males, three females, aged 23–31) using a proposed MSP inversion algorithm across cortical patch ranges (350–3500, step 150). The adaptive patching strategy differs from prior MSP work by dynamically adjusting patch sizes to optimize Free Energy, thereby reducing localization errors more effectively across subject groups.

The proposed MSP inversion achieved reliable EEG source localization, with mean errors ranging from 1.2 to 7.7 mm and a group mean of 4.0 mm, consistently identifying occipitotemporal regions (BA 18, 19, 20, 37). The demonstrated results confirm the accurate localization of visually evoked sources, supporting its potential for diagnostic and computational applications in biomedical engineering and neuroscience.

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Author Biography

  • Anwer Ahmed Memon, Department of Electrical Engineering, Indus University, Karachi, Pakistan

    Dr Engr. Anwar Ahmed Memon

    Associate Professor & Chairperson

    Department of Electrical Engineering

    faculty of engineering, Science & Technology

    Indus University Karachi

     

References

[1] G. Baghdadi, F. Hadaeghi, and C. Kamarajan, "Multimodal approaches to investigating neural dynamics in cognition and related clinical conditions: integrating EEG, MEG, and fMRI data," vol. 19, ed: Frontiers Media SA, 2025, p. 1495018.

[2] M. Awais, N. Badruddin, and M. Drieberg, "A hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability," Sensors, vol. 17, no. 9, p. 1991, 2017.

[3] M. Sadeghi, B. Rivet, and M. Babaie-Zadeh, "Enhancing source separation quality via optimal sensor placement in noisy environments," Signal Processing, vol. 226, p. 109659, 2025.

[4] S. Asadzadeh, T. Y. Rezaii, S. Beheshti, A. Delpak, and S. Meshgini, "A systematic review of EEG source localization techniques and their applications on diagnosis of brain abnormalities," Journal of neuroscience methods, vol. 339, p. 108740, 2020.

[5] P. Vanderhaeghen and F. Polleux, "Developmental mechanisms underlying the evolution of human cortical circuits," Nature Reviews Neuroscience, vol. 24, no. 4, pp. 213-232, 2023.

[6] Z. Feng, I. Kakkos, G. K. Matsopoulos, C. Guan, and Y. Sun, "Explaining E/MEG Source Imaging and Beyond: An Updated Review," IEEE Journal of Biomedical and Health Informatics, 2025.

[7] U. Chaudhary, "Non-invasive Brain Signal Acquisition Techniques: Exploring EEG, EOG, fNIRS, fMRI, MEG, and fUS," in Expanding Senses using Neurotechnology: Volume 1‒Foundation of Brain-Computer Interface Technology: Springer, 2025, pp. 25-80.

[8] F. Caiado and A. Ukolov, "The history, current state and future possibilities of the non-invasive brain computer interfaces," Medicine in Novel Technology and Devices, p. 100353, 2025.

[9] W. A. Wartman et al., "Fast EEG/MEG BEM-based forward problem solution for high-resolution head models," NeuroImage, vol. 306, p. 120998, 2025.

[10] O. L. Elvetun and N. Sudheer, "Weighted sparsity regularization for solving the inverse EEG problem: A case study," Biomedical Signal Processing and Control, vol. 107, p. 107673, 2025.

[11] M. A. Jatoi and N. Kamel, Brain source localization using EEG signal analysis. CRC Press, 2017.

[12] L. Hecker, A. Giri, D. Pantazis, and A. Adler, "Flexible alternating projection for spatially extended brain source localization," IEEE Transactions on Biomedical Engineering, 2025.

[13] M. A. Jatoi, N. Kamel, A. S. Malik, I. Faye, and T. Begum, "A survey of methods used for source localization using EEG signals," Biomedical Signal Processing and Control, vol. 11, pp. 42-52, 2014.

[14] M. H. Soomro, N. Badruddin, M. Z. Yusoff, and M. A. Jatoi, "Automatic eye-blink artifact removal method based on EMD-CCA," in Complex Medical Engineering (CME), 2013 ICME International Conference on, 2013: IEEE, pp. 186-190.

[15] M. Soufineyestani, D. Dowling, and A. Khan, "Electroencephalography (EEG) technology applications and available devices," Applied Sciences, vol. 10, no. 21, p. 7453, 2020.

[16] A. Giri, L. Hecker, J. C. Mosher, A. Adler, and D. Pantazis, "Localization of Realistic Spatial Patches of Complex Source Activity in MEG," bioRxiv, p. 2025.06. 04.657819, 2025.

[17] S. Wang, C. Wei, K. Lou, D. Gu, and Q. Liu, "Advancing eeg/meg source imaging with geometric-informed basis functions," in 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024: IEEE, pp. 1-4.

[18] T. Mannepalli and A. Routray, "Sparse algorithms for EEG source localization," Medical & Biological Engineering & Computing, vol. 59, no. 11, pp. 2325-2352, 2021.

[19] C. Wei, K. Lou, Z. Wang, M. Zhao, D. Mantini, and Q. Liu, "Edge sparse basis network: a deep learning framework for EEG source localization," in 2021 International Joint Conference on Neural Networks (IJCNN), 2021: IEEE, pp. 1-8.

[20] M. Awais, N. Badruddin, and M. Drieberg, "Driver drowsiness detection using EEG power spectrum analysis," in 2014 IEEE Region 10 symposium, 2014: IEEE, pp. 244-247.

[21] M. A. Jatoi and N. Kamel, "Brain source localization using reduced EEG sensors," Signal, Image and video processing, vol. 12, no. 8, pp. 1447-1454, 2018.

[22] M. A. Jatoi, F. A. Dharejo, and S. H. Teevino, "Comparison of machine learning techniques based brain source localization using EEG signals," Current Medical Imaging, vol. 17, no. 1, pp. 64-72, 2021.

[23] M. Jiao, S. Yang, X. Xian, N. Fotedar, and F. Liu, "Multi-modal electrophysiological source imaging with attention neural networks based on deep fusion of EEG and MEG," IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024.

[24] M. A. Jatoi, N. Kamel, A. S. Malik, I. Faye, J. M. Bornot, and T. Begum, "EEG‐based brain source localization using visual stimuli," International Journal of Imaging Systems and Technology, vol. 26, no. 1, pp. 55-64, 2016.

[25] F. L. Da Silva, "EEG: origin and measurement," in EEG-fMRI: physiological basis, technique, and applications: Springer, 2023, pp. 23-48.

[26] G. Marrazzo, F. De Martino, A. Mukovskiy, M. A. Giese, and B. de Gelder, "Neural encoding of biomechanically (im) possible human movements in occipitotemporal cortex," bioRxiv, p. 2025.01. 07.631720, 2025.

[27] J. D. López, J. J. Espinosa, and G. R. Barnes, "Random location of multiple sparse priors for solving the MEG/EEG inverse problem," in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012: IEEE, pp. 1534-1537.

[28] K. Liu et al., "Vssi-ggd: A variation sparse eeg source imaging approach based on generalized gaussian distribution," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 1524-1534, 2024.

[29] K. Friston, J. Kilner, and L. Harrison, "A free energy principle for the brain," Journal of physiology-Paris, vol. 100, no. 1-3, pp. 70-87, 2006.

[30] K. Friston, "The free-energy principle: a unified brain theory?," Nature reviews neuroscience, vol. 11, no. 2, pp. 127-138, 2010.

[31] K. Friston et al., "Multiple sparse priors for the M/EEG inverse problem," NeuroImage, vol. 39, no. 3, pp. 1104-1120, 2008.

[32] G. Flandin and K. J. Friston, "Analysis of family‐wise error rates in statistical parametric mapping using random field theory," Human brain mapping, 2016.

[33] N. An et al., "Imaging somatosensory cortex responses measured by OPM-MEG: Variational free energy-based spatial smoothing estimation approach," Iscience, vol. 25, no. 2, p. 103752, 2022.

[34] K. Friston, J. Mattout, N. Trujillo-Barreto, J. Ashburner, and W. Penny, "Variational free energy and the Laplace approximation," Neuroimage, vol. 34, no. 1, pp. 220-234, 2007.

[35] M. A. Jatoi, N. Kamel, and J. D. López, "Multiple sparse priors technique with optimized patches for brain source localization," International Journal of Imaging Systems and Technology, vol. 30, no. 1, pp. 154-167, 2020.

[36] J. D. López Hincapié, G. R. Barnes, and J. J. Espinosa, "Single meg/eeg source reconstruction with multiple sparse priors and variable patches," Dyna, 2012.

[37] J. D. López, V. Litvak, J. J. Espinosa, K. Friston, and G. R. Barnes, "Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM," NeuroImage, vol. 84, pp. 476-487, 2014.

[38] M. A. Jatoi, N. Kamel, A. S. Malik, I. Faye, and T. Begum, "Representing EEG source localization using finite element method," in Control System, Computing and Engineering (ICCSCE), 2013 IEEE International Conference on, 2013: IEEE, pp. 168-172.

[39] B. Gohel and M. Khare, "EEG/MEG source imaging in the absence of subject's brain MRI scan: Perspective on co‐registration and MRI selection approach," International Journal of Imaging Systems and Technology, vol. 33, no. 1, pp. 287-298, 2023.

[40] V. P. Oikonomou and I. Kompatsiaris, "A novel bayesian approach for eeg source localization," Computational Intelligence and Neuroscience, vol. 2020, 2020.

[41] U. Chaudhary, "Preprocessing Techniques for Brain Signal Data: Noise Reduction and Artifact Removal Methods," in Expanding Senses using Neurotechnology: Volume 1‒Foundation of Brain-Computer Interface Technology: Springer, 2025, pp. 119-178.

[42] A. L. Rincon and S. Shimoda, "The inverse problem in electroencephalography using the bidomain model of electrical activity," Journal of neuroscience methods, vol. 274, pp. 94-105, 2016.

[43] S. M. M. Basri, H. Al-Nashash, and H. Mir, "Inverse problem for M/EEG source localization: a review," IEEE Sensors Journal, 2024.

[44] G. Flandin and K. J. Friston, "Statistical parametric mapping (SPM)," Scholarpedia, vol. 3, no. 4, 2008.

[45] D. G. Wakeman and R. N. Henson, "A Parametric Empirical Bayesian Framework for the EEG/MEG Inverse Problem: Generative Models for Multi-Subject and Multi-Modal Integration," Scientific Data, vol. 2, p. 150001, 2011.

Published

2025-10-28

Issue

Vol. 44 No. 4 (2025): October Issue

Section

Articles

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Copyright (c) 2025 Mehran University Research Journal of Engineering and Technology

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Group-Based EEG Source Mapping of the Visual Cortex Using Adaptive MSP Localization. (2025). Mehran University Research Journal of Engineering and Technology, 44(4), 32-45. https://doi.org/10.22581/muet1982.0176

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