Enhancing Neurophysiological Analysis and Emotion Recognition with EEG Channel Spatial Metadata through Graph Neural Networks

Leonid Sidorov; Archil Maysuradze

doi:10.18287/JBPE24.10.020304

Authors

Leonid Sidorov Lomonosov Moscow State University, Russia
Archil Maysuradze Lomonosov Moscow State University, Russia

DOI:

https://doi.org/10.18287/JBPE24.10.020304

Keywords:

multimodal data integration, multivariate time series, electroencephalogram, spatial data, deep learning, graph neural network, P300 wave, emotion recognition

Abstract

The proposed architecture leverages the recording device’s metadata by encoding the initial position of the recording electrodes into a graph structure which is then processed by the corresponding neural network architecture. This approach has proven its merit in neurophysiological applications such as P300 pattern detection and emotion recognition. Our experiments highlight the crucial role of the coordinate graph within the algorithm, which drastically influences the performance and efficacy of the model. Additionally, the versatility of our model is showcased through its consistent performance across diverse tasks, confirming its potential as a robust framework for future EEG-based research. Further analysis reveals that the incorporation of graph-based data alongside advanced optimization strategies markedly enhances the model’s ability to generalize, making it a valuable asset for the neuroscience community.

References

B Blankertz, KR Muller, DJ Krusienski, G Schalk, JR Wolpaw, A Schlogl, G Pfurtscheller, Jd. R. Millan, M. Schroder, and N. Birbaumer, “The bci competition iii: Validating alternative approaches to actual bci problems,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 14(2), 153–159 (2006).

H. Cecotti, A. Graser, “Convolutional neural networks for p300 detection with application to brain-computer interfaces,” IEEE Transactions on Pattern Analysis and Machine Intelligence 33(3), 433–445 (2010).

P. Mathur, T. Mittal, and D. Manocha, “Dynamic graph modeling of simultaneous eeg and eye-tracking data for reading task identification,” ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1250–1254 (2021).

W. Ye, Z. Zhang, M. Zhang, F. Teng, L. Zhang, L. Li, G. Huang, J. Wang, D. Ni, and Z. Liang, “Semi-supervised dual-stream self-attentive adversarial graph contrastive learning for cross-subject eeg- based emotion recognition,” arXiv preprint arXiv:2308.11635 (2023).

M. Jin, E. Zhu, C. Du, H. He, and J. Li, “Pgcn: Pyramidal graph convolutional network for eeg emotion recognition,” arXiv preprint arXiv:2302.02520 (2023).

P. Hagmann, L. Cammoun, X. Gigandet, R. Meuli, C. J. Honey, V. J. Wedeen, and O. Sporns, “Mapping the structural core of human cerebral cortex,” PLoS Biology 6(7), e159 (2008).

G. E. Bruder, J. W. Stewart, and P. J. McGrath, “Right brain, left brain in depressive disorders: clinical and theoretical implications of behav- ioral, electrophysiological and neuroimaging findings,” Neuroscience & Biobehavioral Reviews 78, 178–191 (2017).

W. L. Zheng, B.-L. Lu, “Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks,” IEEE Transactions on Autonomous Mental Development 7(3), 162–175 (2015).

R. N. Duan, J. Y. Zhu, and B. L. Lu, “Differential entropy feature for EEG-based emotion classification,” 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER), 81–84 (2013).

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Communications of the ACM 60(6), 84–90 (2017).

K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” arXiv preprint arXiv:1810.00826 (2018).

B. Delaunay, “Sur la sphere vide,” Izvestia Akademicheskih Nauk SSSR, Otdelenie Matematicheskii i Estestvennyka Nauk 7(793-800), 1–2 (1934).

F. Sharbrough, G. E. Chatrian, R. Lesser, H. Luders, M. Nuwer, and T. W. Picton, “American electroencephalographic society guidelines for standard electrode position nomenclature,” Journal of Clinical Neurophysiology 8(2), 200–202 (1991).