Sparse Bayesian Learning for End-to-End EEG Decoding

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成果类型：

期刊论文

作者：

Wang, Wenlong;Qi, Feifei;Wipf, David Paul;Cai, Chang;Yu, Tianyou;...

通讯作者：

Yu, ZL;Wu, W

作者机构：

[Yu, Tianyou; Yu, Zhuliang; Li, Yuanqing; Wang, Wenlong] South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China.

[Qi, Feifei; Yu, Tianyou; Yu, Zhuliang; Li, Yuanqing; Wang, Wenlong] Pazhou Lab, Guangzhou 510330, Guangdong, Peoples R China.

[Qi, Feifei] Guangdong Univ Finance, Sch Internet Finance & Informat Engn, Guangzhou 510521, Guangdong, Peoples R China.

[Wipf, David Paul] Amazon Shanghai AI Lab, Shanghai 200336, Peoples R China.

[Cai, Chang] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.

通讯机构：

[Wu, W ] A

[Yu, ZL ] S

South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China.

Pazhou Lab, Guangzhou 510330, Guangdong, Peoples R China.

Alto Neurosci Inc, Loa Altos, CA 94022 USA.

语种：

英文

关键词：

Electroencephalography;Decoding;Classification algorithms;Finite impulse response filters;Filtering algorithms;Brain modeling;Feature extraction;Electroencephalography (EEG);brain-computer interface (BCI);emotion recognition;decoding;spatio-temporal filtering;sparse Bayesian learning

期刊：

IEEE Transactions on Pattern Analysis and Machine Intelligence

ISSN：

0162-8828

年：

2023

卷：

期：

页码：

15632-15649

DOI：

10.1109/TPAMI.2023.3299568

基金类别：

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61836003, 61876063, 61906048 and 62007013) Technology Innovation 2030 (Grant Number: 2022ZD0211700)

机构署名：

本校为其他机构

院系归属：

国家数字化学习工程技术研究中心

摘要：

Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG decoding has gained widespread popularity in recent years owing to the remarkable advances in deep learning research. However, many EEG studies suffer from limited sample sizes, making it difficult for existing deep learning models to effectively generalize to highly noisy EEG data. To address this fundamental limitation, this paper proposes a novel end-to-end EEG decoding algorithm that utilizes a low-rank weight matri...

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Sparse Bayesian Learning for End-to-End EEG Decoding

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