{"title":"基于脑功能连接的情感识别线性-非线性特征重建网络","authors":"Baole Fu;Xiangkun Yu;Yinhua Liu","doi":"10.1109/TIM.2025.3565702","DOIUrl":null,"url":null,"abstract":"The electroencephalogram records both the static performance and dynamic changes in the brain electrical activity, with linear and nonlinear features capable of modeling these static and dynamic aspects. However, traditional EEG-based emotion recognition methods rely excessively on linear features (static analysis) without fully considering nonlinear features (dynamic changes). This limitation hinders a comprehensive capture of the complex interactions and dynamic variations between brain regions. This study proposes an EEG emotion recognition method based on a linear-nonlinear feature reconstruction network using the brain functional connectivity. The method comprehensively captures the complex interactions and dynamic changes between brain regions by extracting linear and nonlinear features from the dynamic and static connectivities. To enhance the feature discriminability, a feature self-reconstruction encoder (FSRE) is introduced, which refines the feature representation and improves the accuracy and discrimination of features. A feature interaction and reconstruction (FIR) module uses a bidirectional reconstruction strategy to interact with and reconstruct features based on their relationships, thereby enhancing the correlation between features and target classification. Experiments conducted on the SEED and SEED-IV datasets validate the effectiveness of this method, demonstrating that it achieves higher accuracy and robustness in emotion classification tasks compared with other advanced methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6000,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Linear–Nonlinear Feature Reconstruction Network for Emotion Recognition From Brain Functional Connectivity\",\"authors\":\"Baole Fu;Xiangkun Yu;Yinhua Liu\",\"doi\":\"10.1109/TIM.2025.3565702\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The electroencephalogram records both the static performance and dynamic changes in the brain electrical activity, with linear and nonlinear features capable of modeling these static and dynamic aspects. However, traditional EEG-based emotion recognition methods rely excessively on linear features (static analysis) without fully considering nonlinear features (dynamic changes). This limitation hinders a comprehensive capture of the complex interactions and dynamic variations between brain regions. This study proposes an EEG emotion recognition method based on a linear-nonlinear feature reconstruction network using the brain functional connectivity. The method comprehensively captures the complex interactions and dynamic changes between brain regions by extracting linear and nonlinear features from the dynamic and static connectivities. To enhance the feature discriminability, a feature self-reconstruction encoder (FSRE) is introduced, which refines the feature representation and improves the accuracy and discrimination of features. A feature interaction and reconstruction (FIR) module uses a bidirectional reconstruction strategy to interact with and reconstruct features based on their relationships, thereby enhancing the correlation between features and target classification. 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引用次数: 0
摘要
脑电图记录了脑电活动的静态表现和动态变化,具有线性和非线性特征,能够对这些静态和动态方面进行建模。然而,传统的基于脑电图的情感识别方法过于依赖线性特征(静态分析),而没有充分考虑非线性特征(动态变化)。这一限制阻碍了对大脑区域之间复杂的相互作用和动态变化的全面捕捉。提出了一种基于脑功能连接的线性-非线性特征重构网络的脑电情绪识别方法。该方法通过从动态和静态连接中提取线性和非线性特征,全面捕捉脑区之间复杂的相互作用和动态变化。为了提高特征的可分辨性,引入了特征自重构编码器(FSRE),对特征表示进行了细化,提高了特征的准确率和可分辨性。FIR (feature interaction and reconstruction)模块采用双向重构策略,根据特征之间的关系与特征进行交互和重构,从而增强特征与目标分类之间的相关性。在SEED和SEED- iv数据集上进行的实验验证了该方法的有效性,表明与其他先进方法相比,该方法在情绪分类任务中具有更高的准确性和鲁棒性。
Linear–Nonlinear Feature Reconstruction Network for Emotion Recognition From Brain Functional Connectivity
The electroencephalogram records both the static performance and dynamic changes in the brain electrical activity, with linear and nonlinear features capable of modeling these static and dynamic aspects. However, traditional EEG-based emotion recognition methods rely excessively on linear features (static analysis) without fully considering nonlinear features (dynamic changes). This limitation hinders a comprehensive capture of the complex interactions and dynamic variations between brain regions. This study proposes an EEG emotion recognition method based on a linear-nonlinear feature reconstruction network using the brain functional connectivity. The method comprehensively captures the complex interactions and dynamic changes between brain regions by extracting linear and nonlinear features from the dynamic and static connectivities. To enhance the feature discriminability, a feature self-reconstruction encoder (FSRE) is introduced, which refines the feature representation and improves the accuracy and discrimination of features. A feature interaction and reconstruction (FIR) module uses a bidirectional reconstruction strategy to interact with and reconstruct features based on their relationships, thereby enhancing the correlation between features and target classification. Experiments conducted on the SEED and SEED-IV datasets validate the effectiveness of this method, demonstrating that it achieves higher accuracy and robustness in emotion classification tasks compared with other advanced methods.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.