Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection

• URL: http://arxiv.org/abs/2501.15062v1
• Date: Sat, 25 Jan 2025 03:46:38 GMT
• Title: Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection
• Authors: Meiyan Xu, Qingqing Chen, Duo Chen, Yi Ding, Jingyuan Wang, Peipei Gu, Yijie Pan, Deshuang Huang, Xun Zhang, Jiayang Guo,
• Abstract summary: EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. Due to the data's non-Euclidean characteristics, existing CNNs may lose important spatial information from EEG, specifically channel correlation. We propose the node-holistic graph convolutional network (NHGNet), a model that uses graphic convolution to dynamically learn each channel's features.
• Score: 17.4184523526021
• License:
• Abstract: EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. In the past decade, with the advancement of deep learning, convolutional neural networks (CNN) have been increasingly used for EEG signal processing. However, due to the data's non-Euclidean characteristics, existing CNNs may lose important spatial information from EEG, specifically channel correlation. Thus, we propose the node-holistic graph convolutional network (NHGNet), a model that uses graphic convolution to dynamically learn each channel's features. With exact fit attention optimization, the network captures inter-channel correlations through a trainable adjacency matrix. The interpretability is enhanced by revealing critical areas of brain activity and their interrelations in various mental states. In validations on two public datasets, NHGNet outperforms the SOTAs. Specifically, in the intra-subject, NHGNet improved detection accuracy by at least 2.34% and 3.42%, and in the inter-subjects, it improved by at least 2.09% and 15.06%. Visualization research on the model revealed that the central parietal area plays an important role in detecting fatigue levels, whereas the frontal and temporal lobes are essential for maintaining vigilance.

Related papers

• Advanced Financial Fraud Detection Using GNN-CL Model [13.5240775562349]
  The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection. It combines the advantages of graph neural networks (gnn), convolutional neural networks (cnn) and long short-term memory (LSTM) networks. A key novelty of this paper is the use of multilayer perceptrons (MLPS) to estimate node similarity.
  arXiv  Detail & Related papers  (2024-07-09T03:59:06Z)
• Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data [54.55126643084341]
  Existing deep learning solutions suffer from three major limitations. We introduce FedGmTE-Net++, a federated graph-based multi-trajectory evolution network. Using the power of federation, we aggregate local learnings among diverse hospitals with limited datasets.
  arXiv  Detail & Related papers  (2024-01-01T10:20:01Z)
• Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy [55.014926694758195]
  Entropy and mutual information in neural networks provide rich information on the learning process. We leverage data geometry to access the underlying manifold and reliably compute these information-theoretic measures. We show that they form noise-resistant measures of intrinsic dimensionality and relationship strength in high-dimensional simulated data.
  arXiv  Detail & Related papers  (2023-12-04T01:32:42Z)
• Optimizing Neural Network Scale for ECG Classification [1.8953148404648703]
  We study scaling convolutional neural networks (CNNs) specifically targeting Residual neural networks (ResNet) for analyzing electrocardiograms (ECGs) We explored and demonstrated an efficient approach to scale ResNet by examining the effects of crucial parameters, including layer depth, the number of channels, and the convolution kernel size. Our findings provide insight into obtaining more efficient and accurate models with fewer computing resources or less time.
  arXiv  Detail & Related papers  (2023-08-24T01:26:31Z)
• EEGSN: Towards Efficient Low-latency Decoding of EEG with Graph Spiking Neural Networks [4.336065967298193]
  A majority of neural networks (SNNs) are trained based on inductive biases that are not necessarily a good fit for several critical tasks that require low-latency and power efficiency. Here, we propose a graph spiking neural architecture for multi-channel EEG classification (EEGS) that learns the dynamic relational information present in the distributed EEG sensors. Our method reduced the inference computational complexity by $times 20$ compared to the state-the-art SNNs, while achieved comparable accuracy on motor execution tasks.
  arXiv  Detail & Related papers  (2023-04-15T23:30:17Z)
• Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networks [6.007238205454907]
  This paper studies the impact of channel impairments on the performance of graph neural networks over the air (AirGNNs) AirGNNs modify graph convolutional operations that shift graph signals over random communication graphs to take into account channel fading and noise. Experiments on decentralized source localization and multi-robot flocking show superior performance of AirGNNs over wireless communication channels.
  arXiv  Detail & Related papers  (2023-02-16T17:40:23Z)
• Influencer Detection with Dynamic Graph Neural Networks [56.1837101824783]
  We investigate different dynamic Graph Neural Networks (GNNs) configurations for influencer detection. We show that using deep multi-head attention in GNN and encoding temporal attributes significantly improves performance.
  arXiv  Detail & Related papers  (2022-11-15T13:00:25Z)
• Gaze-Guided Class Activation Mapping: Leveraging Human Attention for Network Attention in Chest X-rays Classification [3.8637285238278434]
  This paper describes a gaze-guided class activation mapping (GG-CAM) method to directly regulate the formation of network attention. GG-CAM is a lightweight ($3$ additional trainable parameters for regulating the learning process) and generic extension that can be easily applied to most classification convolutional neural networks (CNN) Comparative experiments suggest that two standard CNNs with the GG-CAM extension achieve significantly greater classification performance.
  arXiv  Detail & Related papers  (2022-02-15T00:33:23Z)
• 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)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Ventral-Dorsal Neural Networks: Object Detection via Selective Attention [51.79577908317031]
  We propose a new framework called Ventral-Dorsal Networks (VDNets) Inspired by the structure of the human visual system, we propose the integration of a "Ventral Network" and a "Dorsal Network" Our experimental results reveal that the proposed method outperforms state-of-the-art object detection approaches.
  arXiv  Detail & Related papers  (2020-05-15T23:57:36Z)

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.