A Novel EEG-Based Parkinson’s Disease Detection Model Using Multiscale Convolutional Prototype Networks
Author:
Affiliation:
1. School of Software, South China Normal University, Guangzhou, China
Funder
National Natural Science Foundation of China
Science and Technology Innovation (STI) 2030-Major Projects
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/19/10367905/10385211.pdf?arnumber=10385211
Reference55 articles.
1. Multimodal brain and retinal imaging of dopaminergic degeneration in Parkinson disease
2. Comparative Study of Wearable Sensors, Video, and Handwriting to Detect Parkinson’s Disease
3. Update of the MDS research criteria for prodromal Parkinson's disease
4. Accuracy of clinical diagnosis of Parkinson disease: A systematic review and meta-analysisAuthor Response
5. Detection and Classification of Early Stages of Parkinson’s Disease Through Wearable Sensors and Machine Learning
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1. A Novel Parameter-Free Attention-based Multiscale Convolution Prototype Networks for P300 Brain-computer Interface;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
2. ParViT: A modified Vision Transformer architecture for Parkinson’s Disease identification using EEG signals;2024 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES);2024-05-03
3. A Relevant Prototype Domain Gradient Projection Continual Learning Method for Cross-Subject P300 Brain-Computer Interfaces;Lecture Notes in Computer Science;2024
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