Graph-Theoretic Analysis of Electroencephalography Functional Connectivity Using Phase Lag Index for Detection of Ictal States

Ghansyamkumar Rathod; Hardik Modi

doi:10.35882/jeeemi.v8i1.1230

Ghansyamkumar Rathod Department of Electronics and Communication, Chandubhai S Patel Institute of Technology, Charotar University of Science and Technology, Gujarat, India https://orcid.org/0000-0001-9613-2988
Hardik Modi Department of Electronics and Communication, Chandubhai S Patel Institute of Technology, Charotar University of Science and Technology, Gujarat, India https://orcid.org/0000-0002-1847-4234

DOI: https://doi.org/10.35882/jeeemi.v8i1.1230

Keywords: Phase Lag Index (PLI), Graph Theory, Electroencephalography (EEG), Seizure or Ictal state, Clustering coefficient, Degree Centrality

Abstract

Epileptic disorders are characterized by the misfiring of neurons and affect 50 million people worldwide, who have to live with physical challenges in their normal lives. The ionic activity of the brain can be detected as an electrical activity from the scalp using a non-invasive bio-potential measurement technique known as electroencephalography (EEG). Manual interpretation of brainwaves is a time-consuming, expert-intensive task. In recent years, AI has achieved remarkable results, but at the cost of large datasets and high processing power. We used publicly available online datasets from the Children’s Hospital Boston (CHB) in collaboration with the Massachusetts Institute of Technology (MIT). The datasets consisted of 23 bipolar channels that included pre-processed epochs of both normal and pre-labeled seizure (ictal) states. Using the Phase Lag Index (PLI), the functional connectivity of the network was built to record consistent phase synchronization while minimizing artifacts from volume conduction. Graph-theory-based features were used to detect the brain's seizure state. A significant increase in the values of graph theoretical features, such as degree centrality and clustering coefficient, was observed, along with the formation of hyper-connected hubs and disrupted brain communication in the ictal state. Statistical tests (T-tests, ANOVA, Mann-Whitney U) across multiple PLI thresholds confirmed consistent significant differences (p-value < 0.05) between normal and ictal conditions. This study aims to provide a method based on graph theory, which is computationally efficient, interpretable, and suitable for real-time seizure detection. Considering the efficiency of clustering coefficient and degree of centrality, we can say that they are useful biomarkers for biomedical applications.

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Author Biography

Hardik Modi, Department of Electronics and Communication, Chandubhai S Patel Institute of Technology, Charotar University of Science and Technology, Gujarat, India

Electronics and Communication Engineering Department,

Associate Professor

References

D. Pascual, D. Atienza, R. Wattenhofer, A. Amirshahi, A. Aminifar, and P. Ryvlin, “EpilepsyGAN: Synthetic epileptic brain activities with privacy preservation,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 8, pp. 2435–2446, Dec. 2020.

K. S. Shekokar and S. Dour, “Automatic epileptic seizure detection using LSTM networks,” World Journal of Engineering, vol. 19, no. 2, pp. 224–229, Aug. 2021.

D. Liu, X. Dong, W. Zhou, and D. Bian, “Epileptic seizure prediction using attention augmented convolutional network,” International Journal of Neural Systems, vol. 33, no. 11, Sept. 2023.

S. Skaria and S. K. Savithriamma, “Automatic classification of seizure and seizure-free EEG signals based on phase space reconstruction features,” Journal of Biological Physics, vol. 50, no. 2, pp. 181–196, Mar. 2024.

I.B. Slimen, H. Seddik, Z. Mbarki, and L. Boubchir, “EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms,” Journal of Biomedical Research, vol. 34, no. 3, p. 151, May 2020.

G. Yogarajan et al., “EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network,” Scientific Reports, vol. 13, no. 1, Oct. 2023.

Z. Huang et al., “EEG detection and recognition model for epilepsy based on dual attention mechanism,” Scientific Reports, vol. 15, no. 1, Mar. 2025.

Z. Wang, J. Zhang, Y. Zhao, and Y. He, “Phase lag index-based graph attention networks for detecting driving fatigue,” Review of Scientific Instruments, vol. 92, no. 9, p. 094105, Sept. 2021.

A. Mahajan, M. Sameer, and K. Somaraj, “Adopting artificial intelligence powered ConvNet to detect epileptic seizures,” in Proc. IEEE Conf., 2021.

Y. Gao, Y. Zhang, J. Liu, B. Gao, and Q. Chen, “Deep convolutional neural network-based epileptic electroencephalogram (EEG) signal classification,” Frontiers in Neurology, vol. 11, May 2020.

S. Sheykhivand, A. Farzamnia, A. Delpak, T. Y. Rezaii, and Z. Mousavi, “Automatic identification of epileptic seizures from EEG signals using sparse representation-based classification,” IEEE Access, vol. 8, pp. 138834–138845, Jan. 2020.

Q. Sun, Y. Liu, and S. Li, “Weighted directed graph-based automatic seizure detection with effective brain connectivity for EEG signals,” Signal, Image and Video Processing, vol. 18, no. 1, pp. 899–909, Oct. 2023.

Y. Tang, Q. Wu, H. Mao, and L. Guo, “Epileptic seizure detection based on path signature and Bi-LSTM network with attention mechanism,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 304–313, Jan. 2024.

X. Wang et al., “One dimensional convolutional neural networks for seizure onset detection using long-term scalp and intracranial EEG,” Neurocomputing, vol. 459, pp. 212–222, June 2021.

F. Hassan, S. M. Qaisar, and S. F. Hussain, “Epileptic seizure detection using a hybrid 1D CNN-machine learning approach from EEG data,” Journal of Healthcare Engineering, vol. 2022, pp. 1–16, Nov. 2022.

C. Tian and F. Zhang, “EEG-based epilepsy detection with graph correlation analysis,” Frontiers in Medicine, vol. 12, Mar. 2025.

Y. Yan et al., “Functional connectivity alterations based on the weighted phase lag index: An exploratory electroencephalography study on Alzheimer’s disease,” Current Alzheimer Research, vol. 18, no. 6, pp. 513–522, May 2021.

N. Feng, B. Zhou, H. Wang, and F. Hu, “Motor intention decoding from the upper limb by graph convolutional network based on functional connectivity,” International Journal of Neural Systems, vol. 31, no. 12, Oct. 2021.

W. Bomela, J. S. Li, S. Wang, and C. A. Chou, “Real-time inference and detection of disruptive EEG networks for epileptic seizures,” Scientific Reports, vol. 10, no. 1, May 2020.

Y. Pan et al., “Epileptic seizure detection with hybrid time-frequency EEG input: A deep learning approach,” Computational and Mathematical Methods in Medicine, vol. 2022, suppl. 2, pp. 1–14, Feb. 2022.

N. Zhao, X. Zhou, S. Yang, H. Wang, J. Wang, and T. Luo, “A novel method to identify key nodes in complex networks based on degree and neighborhood information,” Applied Sciences, vol. 14, no. 2, p. 521, Jan. 2024.

K. Zhang et al., “Towards identifying influential nodes in complex networks using semi-local centrality metrics,” Journal of King Saud University – Computer and Information Sciences, vol. 35, no. 10, p. 101798, Oct. 2023.

Y. V. Nandini, T. J. Lakshmi, M. K. Enduri, and H. Sharma, “Link prediction in complex networks using average centrality-based similarity score,” Entropy, vol. 26, no. 6, p. 433, May 2024.

L. Mao et al., “Frontotemporal phase lag index correlates with seizure severity in patients with temporal lobe epilepsy,” Frontiers in Neurology, vol. 13, Dec. 2022.

R. Coa et al., “Estimated EEG functional connectivity and aperiodic component induced by vagal nerve stimulation in patients with drug-resistant epilepsy,” Frontiers in Neurology, vol. 13, Nov. 2022.

M. Jia et al., “Efficient graph convolutional networks for seizure prediction using scalp EEG,” Frontiers in Neuroscience, vol. 16, Aug. 2022.

A. Hajisafi, H. Lin, Y. Y. Chiang, and C. Shahabi, “Dynamic GNNs for precise seizure detection and classification from EEG data,” in Proc. Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD), vol. 14648, pp. 207–220, Jan. 2024.

X. Liu et al., “Epileptic seizure prediction based on EEG using pseudo-three-dimensional CNN,” Frontiers in Neuroinformatics, vol. 18, Mar. 2024.

X. Zhang, X. Zhang, Q. Huang, and F. Chen, “A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning,” Frontiers in Neuroscience, vol. 18, Nov. 2024.

Z. Zhang et al., “Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network,” Frontiers in Neuroscience, vol. 17, Jan. 2024.

K. Song, J. Li, Y. Zhu, F. Ren, L. Cao, and Z. G. Huang, “Altered small-world functional network topology in patients with optic neuritis: A resting-state fMRI study,” Disease Markers, vol. 2021, no. 37, pp. 1–9, June 2021.

H. Javaid, E. Kumarnsit, and S. Chatpun, “Age-related alterations in EEG network connectivity in healthy aging,” Brain Sciences, vol. 12, no. 2, p. 218, Feb. 2022.

M. L. M. Zimmermann et al., “The relationship between pathological brain activity and functional network connectivity in glioma patients,” Journal of Neuro-Oncology, vol. 166, no. 3, pp. 523–533, Feb. 2024.

T. Wadhera and M. Mahmud, “Brain functional network topology in autism spectrum disorder: A novel weighted hierarchical complexity metric for electroencephalogram,” IEEE Journal of Biomedical and Health Informatics, pp. 1–8, Jan. 2022.