{"title":"基于脑电图的多尺度特征融合网络","authors":"Chaowen Shen, Akio Namiki","doi":"10.1016/j.knosys.2025.114540","DOIUrl":null,"url":null,"abstract":"<div><div>Motor imagery electroencephalography (MI-EEG) decoding is a crucial component of brain-computer interface (BCI) systems, serving as a valuable tool for motor function rehabilitation and fundamental neuroscience research. However, the strong nonlinearity and non-stationarity of MI-EEG signals make achieving high-precision decoding a challenging task. Current deep learning methods primarily extract the spatiotemporal features of MI-EEG signals while neglecting their potential association with spectral-topological features, thereby limiting the ability to integrate multidimensional information. To address these limitations, this paper proposes a Topology-Aware Multiscale Feature Fusion network (TA-MFF network) for MI-EEG signal decoding. Specifically, we designed a Spectral-Topological Data Analysis-Processing (S-TDA-P) module that leverages persistent homology features to analyze the spatial topological relationships between EEG electrodes and the persistent patterns of neural activity. Then, the Inter Spectral Recursive Attention (ISRA) mechanism is employed to model the correlations between different frequency bands, enhancing critical spectral features while suppressing irrelevant noise. Finally, the Spectral-Topological and Spatio-Temporal Feature Fusion (SS-FF) Unit is employed to progressively integrate topological, spectral, and spatiotemporal features, capturing dependencies across different domains. The experimental results show that the classification accuracy of the proposed model in BCIC-IV-2a, BCIC-IV-2b, and BCIC-III-Iva is 85.87 %, 90.2 %, and 80.5 %, respectively, outperforming the most advanced methods.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114540"},"PeriodicalIF":7.6000,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A topology-aware multiscale feature fusion network for EEG-based motor imagery decoding\",\"authors\":\"Chaowen Shen, Akio Namiki\",\"doi\":\"10.1016/j.knosys.2025.114540\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Motor imagery electroencephalography (MI-EEG) decoding is a crucial component of brain-computer interface (BCI) systems, serving as a valuable tool for motor function rehabilitation and fundamental neuroscience research. However, the strong nonlinearity and non-stationarity of MI-EEG signals make achieving high-precision decoding a challenging task. Current deep learning methods primarily extract the spatiotemporal features of MI-EEG signals while neglecting their potential association with spectral-topological features, thereby limiting the ability to integrate multidimensional information. To address these limitations, this paper proposes a Topology-Aware Multiscale Feature Fusion network (TA-MFF network) for MI-EEG signal decoding. Specifically, we designed a Spectral-Topological Data Analysis-Processing (S-TDA-P) module that leverages persistent homology features to analyze the spatial topological relationships between EEG electrodes and the persistent patterns of neural activity. Then, the Inter Spectral Recursive Attention (ISRA) mechanism is employed to model the correlations between different frequency bands, enhancing critical spectral features while suppressing irrelevant noise. Finally, the Spectral-Topological and Spatio-Temporal Feature Fusion (SS-FF) Unit is employed to progressively integrate topological, spectral, and spatiotemporal features, capturing dependencies across different domains. The experimental results show that the classification accuracy of the proposed model in BCIC-IV-2a, BCIC-IV-2b, and BCIC-III-Iva is 85.87 %, 90.2 %, and 80.5 %, respectively, outperforming the most advanced methods.</div></div>\",\"PeriodicalId\":49939,\"journal\":{\"name\":\"Knowledge-Based Systems\",\"volume\":\"330 \",\"pages\":\"Article 114540\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2025-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge-Based Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950705125015795\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705125015795","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
运动图像脑电图(MI-EEG)解码是脑机接口(BCI)系统的重要组成部分,是运动功能康复和基础神经科学研究的重要工具。然而,MI-EEG信号的强非线性和非平稳性使得实现高精度解码成为一项具有挑战性的任务。目前的深度学习方法主要提取MI-EEG信号的时空特征,而忽略了它们与频谱拓扑特征的潜在关联,从而限制了对多维信息的整合能力。为了解决这些问题,本文提出了一种拓扑感知多尺度特征融合网络(TA-MFF网络)用于脑电信号解码。具体来说,我们设计了一个光谱-拓扑数据分析-处理(S-TDA-P)模块,该模块利用持续同源特征来分析EEG电极与神经活动持续模式之间的空间拓扑关系。然后,利用频谱间递归注意(ISRA)机制对不同频段之间的相关性进行建模,在增强关键频谱特征的同时抑制无关噪声。最后,利用光谱-拓扑和时空特征融合(SS-FF)单元逐步整合拓扑、光谱和时空特征,捕捉不同领域的依赖关系。实验结果表明,该模型在bbic - iv -2a、bbic - iv -2b和bbic - iii - iva中的分类准确率分别为85.87%、90.2%和80.5%,优于目前最先进的方法。
A topology-aware multiscale feature fusion network for EEG-based motor imagery decoding
Motor imagery electroencephalography (MI-EEG) decoding is a crucial component of brain-computer interface (BCI) systems, serving as a valuable tool for motor function rehabilitation and fundamental neuroscience research. However, the strong nonlinearity and non-stationarity of MI-EEG signals make achieving high-precision decoding a challenging task. Current deep learning methods primarily extract the spatiotemporal features of MI-EEG signals while neglecting their potential association with spectral-topological features, thereby limiting the ability to integrate multidimensional information. To address these limitations, this paper proposes a Topology-Aware Multiscale Feature Fusion network (TA-MFF network) for MI-EEG signal decoding. Specifically, we designed a Spectral-Topological Data Analysis-Processing (S-TDA-P) module that leverages persistent homology features to analyze the spatial topological relationships between EEG electrodes and the persistent patterns of neural activity. Then, the Inter Spectral Recursive Attention (ISRA) mechanism is employed to model the correlations between different frequency bands, enhancing critical spectral features while suppressing irrelevant noise. Finally, the Spectral-Topological and Spatio-Temporal Feature Fusion (SS-FF) Unit is employed to progressively integrate topological, spectral, and spatiotemporal features, capturing dependencies across different domains. The experimental results show that the classification accuracy of the proposed model in BCIC-IV-2a, BCIC-IV-2b, and BCIC-III-Iva is 85.87 %, 90.2 %, and 80.5 %, respectively, outperforming the most advanced methods.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.