Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders

• URL: http://arxiv.org/abs/2501.16626v1
• Date: Mon, 13 Jan 2025 17:29:31 GMT
• Title: Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders
• Authors: Aditya Mishra, Ahnaf Mozib Samin, Ali Etemad, Javad Hashemi,
• Abstract summary: GC-VASE is a graph convolutional-based variational autoencoder that leverages contrastive learning for subject representation learning from EEG data.<n>Our method successfully learns robust subject-specific latent representations using the split-latent space architecture tailored for subject identification.
• Score: 20.364067310176054
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose GC-VASE, a graph convolutional-based variational autoencoder that leverages contrastive learning for subject representation learning from EEG data. Our method successfully learns robust subject-specific latent representations using the split-latent space architecture tailored for subject identification. To enhance the model's adaptability to unseen subjects without extensive retraining, we introduce an attention-based adapter network for fine-tuning, which reduces the computational cost of adapting the model to new subjects. Our method significantly outperforms other deep learning approaches, achieving state-of-the-art results with a subject balanced accuracy of 89.81% on the ERP-Core dataset and 70.85% on the SleepEDFx-20 dataset. After subject adaptive fine-tuning using adapters and attention layers, GC-VASE further improves the subject balanced accuracy to 90.31% on ERP-Core. Additionally, we perform a detailed ablation study to highlight the impact of the key components of our method.

Related papers

• AdaptoVision: A Multi-Resolution Image Recognition Model for Robust and Scalable Classification [0.0]
  AdaptoVision is a novel convolutional neural network (CNN) architecture designed to efficiently balance computational complexity and classification accuracy. By leveraging enhanced residual units, depth-wise separable convolutions, and hierarchical skip connections, AdaptoVision significantly reduces parameter count and computational requirements. It achieves state-of-the-art on BreakHis dataset and comparable accuracy levels, notably 95.3% on CIFAR-10 and 85.77% on CIFAR-100, without relying on any pretrained weights.
  arXiv  Detail & Related papers  (2025-04-17T05:23:07Z)
• Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval [49.669503570350166]
  Generative information retrieval (GenIR) is a promising neural retrieval paradigm that formulates document retrieval as a document identifier (docid) generation task. Existing GenIR models suffer from token-level misalignment, where models trained to predict the next token often fail to capture document-level relevance effectively. We propose direct document relevance optimization (DDRO), which aligns token-level docid generation with document-level relevance estimation through direct optimization via pairwise ranking.
  arXiv  Detail & Related papers  (2025-04-07T15:27:37Z)
• FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors [50.131271229165165]
  Federated Learning (FL) has emerged as a promising framework for distributed machine learning. Data heterogeneity resulting from differences across user behaviors, preferences, and device characteristics poses a significant challenge for federated learning. We propose Adaptive Weight Aggregation (FedAWA), a novel method that adaptively adjusts aggregation weights based on client vectors during the learning process.
  arXiv  Detail & Related papers  (2025-03-20T04:49:40Z)
• SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation [81.36747103102459]
  Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications.<n>Current state-of-the-art methods focus on training innovative architectural designs on confined datasets.<n>We investigate the impact of scaling up EHPS towards a family of generalist foundation models.
  arXiv  Detail & Related papers  (2025-01-16T18:59:46Z)
• iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System [24.898026682692688]
  This study explores advanced classification techniques leveraging interpretable fuzzy transfer learning (iFuzzyTL) iFuzzyTL refines input signal processing and classification in a human-interpretable format by integrating fuzzy inference systems and attention mechanisms. The model's efficacy is demonstrated across three datasets.
  arXiv  Detail & Related papers  (2024-10-16T06:07:23Z)
• Erase then Rectify: A Training-Free Parameter Editing Approach for Cost-Effective Graph Unlearning [17.85404473268992]
  Graph unlearning aims to eliminate the influence of nodes, edges, or attributes from a trained Graph Neural Network (GNN)<n>Existing graph unlearning techniques often necessitate additional training on the remaining data, leading to significant computational costs.<n>We propose a two-stage training-free approach, Erase then Rectify (ETR), designed for efficient and scalable graph unlearning.
  arXiv  Detail & Related papers  (2024-09-25T07:20:59Z)
• Heterogeneous Learning Rate Scheduling for Neural Architecture Search on Long-Tailed Datasets [0.0]
  We propose a novel adaptive learning rate scheduling strategy tailored for the architecture parameters of DARTS. Our approach dynamically adjusts the learning rate of the architecture parameters based on the training epoch, preventing the disruption of well-trained representations.
  arXiv  Detail & Related papers  (2024-06-11T07:32:25Z)
• DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation [36.75453713794983]
  We introduce the Diffusion Model to generate stock factors with Transformer architecture (DiffsFormer) When presented with a specific downstream task, we employ DiffsFormer to augment the training procedure by editing existing samples. The proposed method achieves relative improvements of 7.2% and 27.8% in annualized return ratio for the respective datasets.
  arXiv  Detail & Related papers  (2024-02-05T03:54:36Z)
• Robust Learning with Progressive Data Expansion Against Spurious Correlation [65.83104529677234]
  We study the learning process of a two-layer nonlinear convolutional neural network in the presence of spurious features. Our analysis suggests that imbalanced data groups and easily learnable spurious features can lead to the dominance of spurious features during the learning process. We propose a new training algorithm called PDE that efficiently enhances the model's robustness for a better worst-group performance.
  arXiv  Detail & Related papers  (2023-06-08T05:44:06Z)
• Regularization Through Simultaneous Learning: A Case Study on Plant Classification [0.0]
  This paper introduces Simultaneous Learning, a regularization approach drawing on principles of Transfer Learning and Multi-task Learning. We leverage auxiliary datasets with the target dataset, the UFOP-HVD, to facilitate simultaneous classification guided by a customized loss function. Remarkably, our approach demonstrates superior performance over models without regularization.
  arXiv  Detail & Related papers  (2023-05-22T19:44:57Z)
• GIF: A General Graph Unlearning Strategy via Influence Function [63.52038638220563]
  Graph Influence Function (GIF) is a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a $epsilon$-mass perturbation in deleted data. We conduct extensive experiments on four representative GNN models and three benchmark datasets to justify GIF's superiority in terms of unlearning efficacy, model utility, and unlearning efficiency.
  arXiv  Detail & Related papers  (2023-04-06T03:02:54Z)
• Adaptive Fine-Grained Predicates Learning for Scene Graph Generation [122.4588401267544]
  General Scene Graph Generation (SGG) models tend to predict head predicates and re-balancing strategies prefer tail categories. We propose an Adaptive Fine-Grained Predicates Learning (FGPL-A) which aims at differentiating hard-to-distinguish predicates for SGG. Our proposed model-agnostic strategy significantly boosts performance of benchmark models on VG-SGG and GQA-SGG datasets by up to 175% and 76% on Mean Recall@100, achieving new state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-07-11T03:37:57Z)
• Self-Supervised Pre-Training for Transformer-Based Person Re-Identification [54.55281692768765]
  Transformer-based supervised pre-training achieves great performance in person re-identification (ReID) Due to the domain gap between ImageNet and ReID datasets, it usually needs a larger pre-training dataset to boost the performance. This work aims to mitigate the gap between the pre-training and ReID datasets from the perspective of data and model structure.
  arXiv  Detail & Related papers  (2021-11-23T18:59:08Z)
• Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation [95.31590177308482]
  We propose an automated multi-loss adaptation (named Ada-Segment) to flexibly adjust multiple training losses over the course of training. With an end-to-end architecture, Ada-Segment generalizes to different datasets without the need of re-tuning hyper parameters. Ada-Segment brings 2.7% panoptic quality (PQ) improvement on COCO val split from the vanilla baseline, achieving the state-of-the-art 48.5% PQ on COCO test-dev split and 32.9% PQ on ADE20K dataset.
  arXiv  Detail & Related papers  (2020-12-07T11:43:10Z)

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.