A Semi-Supervised Progressive Learning Algorithm for Brain–Computer Interface

Author:

Wei Yuxuan1ORCID,Li Jie1ORCID,Ji Hongfei1ORCID,Jin Lingjing2ORCID,Liu Lingyu2,Bai Zhongfei2,Ye Chen1ORCID

Affiliation:

1. Department of Computer Science and Technology, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Electronic and Information Engineering, Tongji University, Shanghai, China

2. Department of Neurorehabilitation, Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China

Funder

Shanghai Municipal Science and Technology Major Project

Fundamental Research Funds for the Central Universities

Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Reference47 articles.

1. BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data

2. Model-agnostic meta-learning for fast adaptation of deep networks;finn;Proc 34th Int Conf Mach Learn (PMLR),2017

3. Squared-loss mutual information regularization: A novel information-theoretic approach to semi-supervised learning;niu;Proc 30th Int Conf Mach Learn (PMLR),2013

4. A survey on semi-supervised learning

5. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Smart computing in brain-computer interface and neuroscientific research: opportunities, methods, and challenges;Intelligent Computing Techniques in Biomedical Imaging;2025

2. Online semi-supervised learning for motor imagery EEG classification;Computers in Biology and Medicine;2023-10

3. Beyond Supervised Learning for Pervasive Healthcare;IEEE Reviews in Biomedical Engineering;2023

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3