SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification

Wang, Jialin; Gao, Rui; Zheng, Haotian; Zhu, Hao; Shi, C.-J.R.

doi:10.1109/tnnls.2023.3252569

Cited by 12 publications

(2 citation statements)

References 54 publications

(86 reference statements)

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“…We showed that the stability analysis offers a promising solution for performing the attention mechanism in a graph convolutional network faster and more efficiently by reducing the computational complexity, increasing the interpretability, and eliminating sensitivity to hyperparameters. Ranking the stability properties of nodes makes attention models more transparent and explainable and can be applied to a wide range of tasks, including weight pruning [41], sparsification, and reducing the number of non-zero weights in the network [42], making structural bias [43], etc.…”

Section: Discussionmentioning

confidence: 99%

Uncovering the Origins of Instability in Dynamical Systems: How Can the Attention Mechanism Help?

Bahador

Lankarany

2023

Dynamics

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The behavior of the network and its stability are governed by both dynamics of the individual nodes, as well as their topological interconnections. The attention mechanism as an integral part of neural network models was initially designed for natural language processing (NLP) and, so far, has shown excellent performance in combining the dynamics of individual nodes and the coupling strengths between them within a network. Despite the undoubted impact of the attention mechanism, it is not yet clear why some nodes of a network obtain higher attention weights. To come up with more explainable solutions, we tried to look at the problem from a stability perspective. Based on stability theory, negative connections in a network can create feedback loops or other complex structures by allowing information to flow in the opposite direction. These structures play a critical role in the dynamics of a complex system and can contribute to abnormal synchronization, amplification, or suppression. We hypothesized that those nodes that are involved in organizing such structures could push the entire network into instability modes and therefore need more attention during analysis. To test this hypothesis, the attention mechanism, along with spectral and topological stability analyses, was performed on a real-world numerical problem, i.e., a linear Multi-Input Multi-Output state-space model of a piezoelectric tube actuator. The findings of our study suggest that the attention should be directed toward the collective behavior of imbalanced structures and polarity-driven structural instabilities within the network. The results demonstrated that the nodes receiving more attention cause more instability in the system. Our study provides a proof of concept to understand why perturbing some nodes of a network may cause dramatic changes in the network dynamics.

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Section: Discussionmentioning

confidence: 99%

Uncovering the Origins of Instability in Dynamical Systems: How Can the Attention Mechanism Help?

Bahador

Lankarany

2023

Dynamics

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“…EEG signal classification is a fundamental task in the analysis of brain function, which can be considered as one-dimensional biomedical signal processing [ 109 , 110 , 111 ]. Various processing methods can be employed to classify EEG signals, including statistics, machine learning (deep learning), and other techniques.…”

Section: The Pipeline Of Eeg Signal Analysismentioning

confidence: 99%

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

Chaddad

Kateb

et al. 2023

Sensors

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The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain–computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.

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Investigation of the Impact of EEG Signal Processing Techniques on Classification Performance

Babu,

Wadhwani

2024

Lecture Notes in Electrical Engineering

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SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification

Cited by 12 publications

References 54 publications

Uncovering the Origins of Instability in Dynamical Systems: How Can the Attention Mechanism Help?

Uncovering the Origins of Instability in Dynamical Systems: How Can the Attention Mechanism Help?

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

Investigation of the Impact of EEG Signal Processing Techniques on Classification Performance

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