Attention-based Graph ResNet for Motor Intent Detection from Raw EEG
signals
- URL: http://arxiv.org/abs/2007.13484v1
- Date: Thu, 25 Jun 2020 09:29:48 GMT
- Title: Attention-based Graph ResNet for Motor Intent Detection from Raw EEG
signals
- Authors: Shuyue Jia, Yimin Hou, Yan Shi, Yang Li
- Abstract summary: 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.
- Score: 8.775745069873558
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In previous studies, decoding electroencephalography (EEG) signals has not
considered the topological relationship of EEG electrodes. However, the latest
neuroscience has suggested brain network connectivity. Thus, the exhibited
interaction between EEG channels might not be appropriately measured via
Euclidean distance. To fill the gap, an attention-based graph residual network,
a novel structure of Graph Convolutional Neural Network (GCN), was presented to
detect human motor intents from raw EEG signals, where the topological
structure of EEG electrodes was built as a graph. Meanwhile, deep residual
learning with a full-attention architecture was introduced to address the
degradation problem concerning deeper networks in raw EEG motor imagery (MI)
data. Individual variability, the critical and longstanding challenge
underlying EEG signals, has been successfully handled with the state-of-the-art
performance, 98.08% accuracy at the subject level, 94.28% for 20 subjects.
Numerical results were promising that the implementation of the
graph-structured topology was superior to decode raw EEG data. The innovative
deep learning approach was expected to entail a universal method towards both
neuroscience research and real-world EEG-based practical applications, e.g.,
seizure prediction.
Related papers
- 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) - Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data [6.401370088497331]
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.
arXiv Detail & Related papers (2024-05-08T21:36:49Z) - 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) - MAtt: A Manifold Attention Network for EEG Decoding [0.966840768820136]
We propose a novel geometric learning (GDL)-based model for EEG decoding, featuring a manifold attention network (mAtt)
The evaluation of MAtt on both time-synchronous and -asyncronous EEG datasets suggests its superiority over other leading DL methods for general EEG decoding.
arXiv Detail & Related papers (2022-10-05T02:26:31Z) - Task-oriented Self-supervised Learning for Anomaly Detection in
Electroencephalography [51.45515911920534]
A task-oriented self-supervised learning approach is proposed to train a more effective anomaly detector.
A specific two branch convolutional neural network with larger kernels is designed as the feature extractor.
The effectively designed and trained feature extractor has shown to be able to extract better feature representations from EEGs.
arXiv Detail & Related papers (2022-07-04T13:15:08Z) - EEG-Inception: An Accurate and Robust End-to-End Neural Network for
EEG-based Motor Imagery Classification [123.93460670568554]
This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based motor imagery (MI) classification.
The proposed CNN model, namely EEG-Inception, is built on the backbone of the Inception-Time network.
The proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing.
arXiv Detail & Related papers (2021-01-24T19:03:10Z) - 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) - Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor
Imagery Recognition [9.039355687614076]
This paper presents a novel deep learning approach designed towards remarkably accurate and responsive motor imagery (MI) recognition based on scalp EEG.
BiLSTM with the Attention mechanism manages to derive relevant features from raw EEG signals.
The 0.4-second detection framework has shown effective and efficient prediction based on individual and group-wise training, with 98.81% and 94.64% accuracy, respectively.
arXiv Detail & Related papers (2020-05-02T10:03:40Z) - Opportunities and Challenges of Deep Learning Methods for
Electrocardiogram Data: A Systematic Review [62.490310870300746]
The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare.
Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals.
This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
arXiv Detail & Related papers (2019-12-28T02:44:29Z)
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.