Utilizing Normalized Mutual Information as a Similarity Measure for EEG and fMRI Fusion

Rabiei, Z.; Montazery Kordy, H.

doi:10.22061/jecei.2024.10984.754

Articles in Press

Document Type : Original Research Paper

Authors

Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.

https://doi.org/10.22061/jecei.2024.10984.754

Abstract

Background and Objectives: Neuroscience research can benefit greatly from the fusion of simultaneous recordings of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) data due to their complementary properties. We can extract shared information by coupling two modalities in a symmetric data fusion.
Methods: This paper proposed an approach based on the advanced coupled matrix tensor factorization (ACMTF) method for analyzing simultaneous EEG-fMRI data. To alleviate the strict equality assumption of shared factors in the common dimension of the ACMTF, the proposed method used a similarity criterion based on normalized mutual information (NMI). This similarity criterion effectively revealed the underlying relationships between the modalities, resulting in more accurate factorization results.
Results: The suggested method was utilized on simulated data with various levels of correlation between the components of the two modalities. Despite different noise levels, the average match score improved compared to the ACMTF model, as demonstrated by the results.
Conclusion: By relaxing the strict equality assumption, we can identify shared components in a common mode and extract shared components with higher performance than the traditional methods. The suggested method offers a more robust and effective way to analyze multimodal data sets. The findings highlight the potential of the ACMTF method with NMI-based similarity criterion for uncovering hidden patterns in EEG and fMRI data.

Keywords

Main Subjects

Bioelectric

Open Access

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References

[1] Z. Jiang, Y. Liu, W. Li, Y. Dai, L. Zou, "Integration of simultaneous fMRI and EEG source localization in emotional decision problems," Behav. Brain Res., 448: 114445, 2023.

[2] D. Lahat, T. l. Adali, C. Jutten, "Multimodal data fusion: an overview of methods, challenges and prospects," Proc. IEEE, 103: 1449-1477, 2015.

[3] T. Warbrick, "Simultaneous EEG-fMRI: what have we learned and what does the future hold?," Sensors, 22: 2262, 2022.

[4] S. Van Eyndhoven, B. l. Hunyadi, L. De Lathauwer, et al., "Flexible fusion of electroencephalography and functional magnetic resonance imaging: Revealing neural-hemodynamic coupling through structured matrix-tensor factorization," in Proc. 25th European Signal Processing Conference (EUSIPCO): 26-30, 2017.

[5] H. K. Aljobouri, "Independent component analysis with functional neuroscience data analysis," J. Biomed. Phys. Eng., 13: 169, 2023.

[6] J. Sui, D. Zhi, V. D. Calhoun, "Data-driven multimodal fusion: approaches and applications in psychiatric research," Psychoradiology, 3: kkad026, 2023.

[7] L. Du, H. Wang, J. Zhang, S. Zhang, L. Guo, J. Han, A. S. D. N. Initiative, "Adaptive structured sparse multiview canonical correlation analysis for multimodal brain imaging association identification," Sci. China Inf. Sci., 66: 142106, 2023.

[8] R. F. Silva, S. M. Plis, T. l. Adali M. S. Pattichis, V. D. Calhoun, "Multidataset independent subspace analysis with application to multimodal fusion," IEEE Trans. Image Process., 30: 588-602, 2020.

[9] Y. Jonmohamadi, S. Muthukumaraswamy, J. Chen et al., “Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition,” Brain Topogr., 33(5): 636-650, 2020.

[10] G. R. Poudel, R. D. Jones, "Multimodal neuroimaging with simultaneous fMRI and EEG," in Handbook of Neuroengineering: Springer, pp. 1-23, 2021.

[11] E. Acar, T. G. Kolda, D. M. Dunlavy, “All-at-once optimization for coupled matrix and tensor factorizations,” arXiv preprint arXiv:1105.3422, 2011.

[12] E. Acar, M. A. Rasmussen, F. Savorani, et al., “Understanding data fusion within the framework of coupled matrix and tensor factorizations,” Chemom. Intell. Lab. Syst., 129: 53-63, 2013.

[13] E. Acar, G. z. Gurdeniz, M. A. Rasmussen et al., "Coupled matrix factorization with sparse factors to identify potential biomarkers in metabolomics," Int. J. Knowl. Discovery Bioinf. (IJKDB), 3(3): 1-22, 2012.

[14] E. Acar, E. E. Papalexakis, G. z. Gurdeniz et al., “Structure-revealing data fusion,” BMC Bioinf., 15(1): 1-17, 2014.

[15] E. Karahan, P. A. Rojas-Lopez, M. L. Bringas-Vega et al., “Tensor analysis and fusion of multimodal brain images,” Proc. IEEE, 103(9): 1531-1559, 2015.

[16] B. Rivet, M. Duda, A. Guerin-Dugue, et al., "Multimodal approach to estimate the ocular movements during EEG recordings: a coupled tensor factorization method," in Proc. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: 6983-6986, 2015.

[17] C. Chatzichristos, E. Kofidis, L. De Lathauwer et al., “Early soft and flexible fusion of EEG and fMRI via tensor decompositions,” arXiv preprint arXiv:2005.07134, 2020.

[18] R. Mosayebi, G. A. Hossein-Zadeh, “Correlated coupled matrix tensor factorization method for simultaneous EEG-fMRI data fusion,” Biomed. Signal Process. Control, 62: 102071, 2020.

[19] Y. Maeda, H. Kawaguchi, H. Tezuka, "Estimation of mutual information via quantum kernel method," arXiv preprint arXiv:2310.12396, 2023.

[20] M. Babaie-Zadeh, C. Jutten, "A general approach for mutual information minimization and its application to blind source separation," Signal Process., 85: 975-995, 2005.

[21] T. O. Kvalseth, "On normalized mutual information: measure derivations and properties," Entropy, 19: 631, 2017.

[22] A. Cichocki, D. Mandic, L. De Lathauwer, G. Zhou, Q. Zhao, C. Caiafa, H. A. Phan, “Tensor decompositions for signal processing applications: From two-way to multiway component analysis,” IEEE Signal Process. Mag., 32: 145-163, 2015.

[23] N. M. Correa, T. Eichele, T. l. Adali, et al., “Multi-set canonical correlation analysis for the fusion of concurrent single trial ERP and functional MRI,” Neuroimage, 50(4): 1438-1445, 2010.

[24] S. Van Eyndhoven, P. DuPont, S. Tousseyn, et al., “Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI data,” Neuroimage, 228: 117652, 2021.

[25] M. Morante, "A lite parametric model for the hemodynamic response function," arXiv preprint arXiv:2004.13361, 2020.

[26] D. A. Handwerker, J. M. Ollinger, M. D'Esposito, "Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses," Neuroimage, 21: 1639-1651, 2004.

[27] Z. Y. Shan, M. J. Wright, P. M. Thompson, K. L. McMahon, G. G. Blokland, G. I. De Zubicaray, N. G. Martin, A. A. Vinkhuyzen, D. C. Reutens, "Modeling of the hemodynamic responses in block design fMRI studies," J. Cereb. Blood Flow Metab., 34: 316-324, 2014.

[28] M. W. Woolrich, T. E. Behrens, S. M. Smith, "Constrained linear basis sets for HRF modeling using Variational Bayes," Neuroimage, 21: 1748-1761, 2004.

[29] C. Gössl, L. Fahrmeir, D. P. Auer, "Bayesian modeling of the hemodynamic response function in BOLD fMRI," Neuroimage, 14: 140-148, 2001.

[30] C. Goutte, F. A. Nielsen, K. Hansen, "Modeling the hemodynamic response in fMRI using smooth FIR filters," IEEE Trans. Med. Imaging, 19: 1188-1201, 2000.

[31] H. Mohimani, M. Babaie-Zadeh, C. Jutten, "A fast approach for overcomplete sparse decomposition based on smoothed -norm," IEEE Trans. Signal Process., 57: 289-301, 2008.

[32] M. Babaie-Zadeh, C. Jutten, K. Nayebi, "Differential of the mutual information," IEEE Signal Process. Lett., 11: 48-51, 2004.

[33] J. M. Walz, R. I. Goldman, M. Carapezza, J. Muraskin, T. R. Brown, P. Sajda, "Simultaneous EEG-fMRI reveals a temporal cascade of task-related and default-mode activations during a simple target detection task," Neuroimage, 102: 229-239, 2014.

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Utilizing Normalized Mutual Information as a Similarity Measure for EEG and fMRI Fusion

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References

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Articles in Press, Accepted Manuscript
Available Online from 21 October 2024

Utilizing Normalized Mutual Information as a Similarity Measure for EEG and fMRI Fusion

References

References

Send comment about this article

Articles in Press, Accepted Manuscript Available Online from 21 October 2024

Articles in Press, Accepted Manuscript
Available Online from 21 October 2024