Shared Attention-based Autoencoder with Hierarchical Fusion-based Graph Convolution Network for sEEG SOZ Identification

• URL: http://arxiv.org/abs/2412.12651v1
• Date: Tue, 17 Dec 2024 08:20:02 GMT
• Title: Shared Attention-based Autoencoder with Hierarchical Fusion-based Graph Convolution Network for sEEG SOZ Identification
• Authors: Huachao Yan, Kailing Guo, Shiwei Song, Yihai Dai, Xiaoqiang Wei, Xiaofen Xing, Xiangmin Xu,
• Abstract summary: Diagnosing seizure onset zone (SOZ) is a challenge in neurosurgery, where stereoelectroencephalography (sEEG) serves as a critical technique.<n>In sEEG SOZ identification, the existing studies focus solely on the intra-patient representation of epileptic information.<n>We propose the shared attention-based autoencoder (sATAE) to address the aforementioned challenges.
• Score: 10.413397851804152
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diagnosing seizure onset zone (SOZ) is a challenge in neurosurgery, where stereoelectroencephalography (sEEG) serves as a critical technique. In sEEG SOZ identification, the existing studies focus solely on the intra-patient representation of epileptic information, overlooking the general features of epilepsy across patients and feature interdependencies between feature elements in each contact site. In order to address the aforementioned challenges, we propose the shared attention-based autoencoder (sATAE). sATAE is trained by sEEG data across all patients, with attention blocks introduced to enhance the representation of interdependencies between feature elements. Considering the spatial diversity of sEEG across patients, we introduce graph-based method for identification SOZ of each patient. However, the current graph-based methods for sEEG SOZ identification rely exclusively on static graphs to model epileptic networks. Inspired by the finding of neuroscience that epileptic network is intricately characterized by the interplay of sophisticated equilibrium between fluctuating and stable states, we design the hierarchical fusion-based graph convolution network (HFGCN) to identify the SOZ. HFGCN integrates the dynamic and static characteristics of epileptic networks through hierarchical weighting across different hierarchies, facilitating a more comprehensive learning of epileptic features and enriching node information for sEEG SOZ identification. Combining sATAE and HFGCN, we perform comprehensive experiments with sATAE-HFGCN on the self-build sEEG dataset, which includes sEEG data from 17 patients with temporal lobe epilepsy. The results show that our method, sATAE-HFGCN, achieves superior performance for identifying the SOZ of each patient, effectively addressing the aforementioned challenges, providing an efficient solution for sEEG-based SOZ identification.

Related papers

• EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding [71.31963197992998]
  We introduce a large-scale, code-based competition comprising two challenges.<n>The Transfer Challenge asks participants to build and test a model that can zero-shot decode new tasks and new subjects from their EEG data.<n>The Psychopathology factor prediction Challenge asks participants to infer subject measures of mental health from EEG data.
  arXiv  Detail & Related papers  (2025-06-23T21:25:19Z)
• CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information [61.1904164368732]
  We propose CognitionCapturer, a unified framework that fully leverages multimodal data to represent EEG signals.<n>Specifically, CognitionCapturer trains Modality Experts for each modality to extract cross-modal information from the EEG modality.<n>The framework does not require any fine-tuning of the generative models and can be extended to incorporate more modalities.
  arXiv  Detail & Related papers  (2024-12-13T16:27:54Z)
• Decoding Human Emotions: Analyzing Multi-Channel EEG Data using LSTM Networks [0.0]
  This study aims to understand and improve the predictive accuracy of emotional state classification by applying a Long Short-Term Memory (LSTM) network to analyze EEG signals. Using a popular dataset of multi-channel EEG recordings known as DEAP, we look towards leveraging LSTM networks' properties to handle temporal dependencies within EEG signal data. We obtain accuracies of 89.89%, 90.33%, 90.70%, and 90.54% for arousal, valence, dominance, and likeness, respectively, demonstrating significant improvements in emotion recognition model capabilities.
  arXiv  Detail & Related papers  (2024-08-19T18:10:47Z)
• RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier [0.0]
  RISE-iEEG stands for Robust Inter-Subject Electrode Implantation Variability iEEG. We developed an iEEG decoder model that can be applied across multiple patients' data without requiring the coordinates of electrode for each patient. Our analysis shows that the performance of RISE-iEEG is 10% higher than that of HTNet and EEGNet in terms of F1 score.
  arXiv  Detail & Related papers  (2024-08-12T18:33:19Z)
• Parkinson's Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations [9.544065991313062]
  We propose a novel graph neural network (GNN) technique for explainable Parkinson's disease (PD) detection using resting state EEG. We employ structured global convolutions with contrastive learning to better model complex features with limited data. We developed and evaluated our method using the UC San Diego Parkinson's disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation.
  arXiv  Detail & Related papers  (2024-08-01T20:54:33Z)
• EEG decoding with conditional identification information [7.873458431535408]
  Decoding EEG signals is crucial for unraveling human brain and advancing brain-computer interfaces. Traditional machine learning algorithms have been hindered by the high noise levels and inherent inter-person variations in EEG signals. Recent advances in deep neural networks (DNNs) have shown promise, owing to their advanced nonlinear modeling capabilities.
  arXiv  Detail & Related papers  (2024-03-21T13:38:59Z)
• Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition [20.655367200006076]
  This study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals. The graph mixup technique is employed to enhance latent connected edges and mitigate noisy label issues. We evaluate our approach on two widely used datasets, namely SEED and SEEDIV, for emotion recognition tasks.
  arXiv  Detail & Related papers  (2023-10-22T03:47:11Z)
• A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation [48.85731427874065]
  This paper proposes a knowledge-driven cross-view contrastive learning framework (KDC2) to extract effective representations from EEG with limited labels. The KDC2 method creates scalp and neural views of EEG signals, simulating the internal and external representation of brain activity. By modeling prior neural knowledge based on neural information consistency theory, the proposed method extracts invariant and complementary neural knowledge to generate combined representations.
  arXiv  Detail & Related papers  (2023-09-21T08:53:51Z)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• Dynamic Graph Modeling of Simultaneous EEG and Eye-tracking Data for Reading Task Identification [79.41619843969347]
  We present a new approach, that we call AdaGTCN, for identifying human reader intent from Electroencephalogram(EEG) and Eye movement(EM) data. Our method, Adaptive Graph Temporal Convolution Network (AdaGTCN), uses an Adaptive Graph Learning Layer and Deep Neighborhood Graph Convolution Layer. We compare our approach with several baselines to report an improvement of 6.29% on the ZuCo 2.0 dataset, along with extensive ablation experiments.
  arXiv  Detail & Related papers  (2021-02-21T18:19:49Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)
• Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition [8.356765961526955]
  We investigate three functional connectivity network features: strength, clustering, coefficient and eigenvector centrality. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public EEG datasets. We construct a multimodal emotion recognition model by combining the functional connectivity features from EEG and the features from eye movements or physiological signals.
  arXiv  Detail & Related papers  (2020-04-04T16:51:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.