Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data

• URL: http://arxiv.org/abs/2405.09568v1
• Date: Wed, 8 May 2024 21:36:49 GMT
• Title: Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data
• Authors: Arash Hajisafi, Haowen Lin, Yao-Yi Chiang, Cyrus Shahabi,
• Abstract summary: This paper introduces NeuroGNN, a dynamic Graph Neural Network (GNN) framework that captures the interplay between the EEG locations and the semantics of their corresponding brain regions. Our experiments with real-world data demonstrate that NeuroGNN significantly outperforms existing state-of-the-art models.
• Score: 6.401370088497331
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diagnosing epilepsy requires accurate seizure detection and classification, but traditional manual EEG signal analysis is resource-intensive. Meanwhile, automated algorithms often overlook EEG's geometric and semantic properties critical for interpreting brain activity. This paper introduces NeuroGNN, a dynamic Graph Neural Network (GNN) framework that captures the dynamic interplay between the EEG electrode locations and the semantics of their corresponding brain regions. The specific brain region where an electrode is placed critically shapes the nature of captured EEG signals. Each brain region governs distinct cognitive functions, emotions, and sensory processing, influencing both the semantic and spatial relationships within the EEG data. Understanding and modeling these intricate brain relationships are essential for accurate and meaningful insights into brain activity. This is precisely where the proposed NeuroGNN framework excels by dynamically constructing a graph that encapsulates these evolving spatial, temporal, semantic, and taxonomic correlations to improve precision in seizure detection and classification. Our extensive experiments with real-world data demonstrate that NeuroGNN significantly outperforms existing state-of-the-art models.

Related papers

• TAVRNN: Temporal Attention-enhanced Variational Graph RNN Captures Neural Dynamics and Behavior [2.5282283486446757]
  We introduce Temporal Attention-enhanced Variational Graph Recurrent Neural Network (TAVRNN) TAVRNN captures temporal changes in network structure by modeling sequential snapshots of neuronal activity. We show that TAVRNN outperforms previous baseline models in classification, clustering tasks and computational efficiency.
  arXiv  Detail & Related papers  (2024-10-01T13:19:51Z)
• Feature Estimation of Global Language Processing in EEG Using Attention Maps [5.173821279121835]
  This study introduces a novel approach to EEG feature estimation that utilizes the weights of deep learning models to explore this association. We demonstrate that attention maps generated from Vision Transformers and EEGNet effectively identify features that align with findings from prior studies. The application of Mel-Spectrogram with ViTs enhances the resolution of temporal and frequency-related EEG characteristics.
  arXiv  Detail & Related papers  (2024-09-27T22:52:31Z)
• MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning [3.840859750115109]
  We present the MEEG dataset, a multi-modal collection of music-induced electroencephalogram (EEG) recordings. We introduce the Attention-based Temporal Learner with Dynamic Graph Neural Network (AT-DGNN), a novel framework for EEG-based emotion recognition.
  arXiv  Detail & Related papers  (2024-07-08T01:58:48Z)
• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates [54.96885726053036]
  This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks.
  arXiv  Detail & Related papers  (2024-06-03T16:30:19Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• DBGDGM: Dynamic Brain Graph Deep Generative Model [63.23390833353625]
  Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function and dysfunction. Previous works, however, ignore the temporal dynamics of the brain and focus on static graphs. We propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.
  arXiv  Detail & Related papers  (2023-01-26T20:45:30Z)
• 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)
• DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning [58.94034282469377]
  We propose DynDepNet, a novel method for learning the optimal time-varying dependency structure of fMRI data induced by downstream prediction tasks. Experiments on real-world fMRI datasets, for the task of sex classification, demonstrate that DynDepNet achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2022-09-27T16:32:11Z)
• Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks [81.74442855155843]
  We introduce a new classification system that utilizes brain connectivity with a CNN and validate its effectiveness via the emotional video classification. The level of concentration of the brain connectivity related to the emotional property of the target video is correlated with classification performance.
  arXiv  Detail & Related papers  (2021-01-18T13:28:08Z)
• Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals [8.775745069873558]
  In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes. An attention-based graph residual network, a novel structure of Graph Convolutional Neural Network (GCN), was presented to detect human motor intents. Deep residual learning with a full-attention architecture was introduced to address the degradation problem concerning deeper networks in raw EEG motor imagery.
  arXiv  Detail & Related papers  (2020-06-25T09:29:48Z)
• GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals [8.19994663278877]
  A novel deep learning framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals. The introduced approach has been shown to converge for both personalized and group-wise predictions.
  arXiv  Detail & Related papers  (2020-06-16T04:57:12Z)

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.