PDF(Pubmed)
Abstract：
UNASSIGNED: Recognizing emotions from electroencephalography (EEG) signals is a challenging task due to the complex, nonlinear, and nonstationary characteristics of brain activity. Traditional methods often fail to capture these subtle dynamics, while deep learning approaches lack explainability. In this research, we introduce a novel three-phase methodology integrating manifold embedding, multilevel heterogeneous recurrence analysis (MHRA), and ensemble learning to address these limitations in EEG-based emotion recognition.
UNASSIGNED: The proposed methodology was evaluated using the SJTU-SEED IV database. We first applied uniform manifold approximation and projection (UMAP) for manifold embedding of the 62-lead EEG signals into a lower-dimensional space. We then developed MHRA to characterize the complex recurrence dynamics of brain activity across multiple transition levels. Finally, we employed tree-based ensemble learning methods to classify four emotions (neutral, sad, fear, happy) based on the extracted MHRA features.
UNASSIGNED: Our approach achieved high performance, with an accuracy of 0.7885 and an AUC of 0.7552, outperforming existing methods on the same dataset. Additionally, our methodology provided the most consistent recognition performance across different emotions. Sensitivity analysis revealed specific MHRA metrics that were strongly associated with each emotion, offering valuable insights into the underlying neural dynamics.
UNASSIGNED: This study presents a novel framework for EEG-based emotion recognition that effectively captures the complex nonlinear and nonstationary dynamics of brain activity while maintaining explainability. The proposed methodology offers significant potential for advancing our understanding of emotional processing and developing more reliable emotion recognition systems with broad applications in healthcare and beyond.
UNASSIGNED: The proposed methodology was evaluated using the SJTU-SEED IV database. We first applied uniform manifold approximation and projection (UMAP) for manifold embedding of the 62-lead EEG signals into a lower-dimensional space. We then developed MHRA to characterize the complex recurrence dynamics of brain activity across multiple transition levels. Finally, we employed tree-based ensemble learning methods to classify four emotions (neutral, sad, fear, happy) based on the extracted MHRA features.
UNASSIGNED: Our approach achieved high performance, with an accuracy of 0.7885 and an AUC of 0.7552, outperforming existing methods on the same dataset. Additionally, our methodology provided the most consistent recognition performance across different emotions. Sensitivity analysis revealed specific MHRA metrics that were strongly associated with each emotion, offering valuable insights into the underlying neural dynamics.
UNASSIGNED: This study presents a novel framework for EEG-based emotion recognition that effectively captures the complex nonlinear and nonstationary dynamics of brain activity while maintaining explainability. The proposed methodology offers significant potential for advancing our understanding of emotional processing and developing more reliable emotion recognition systems with broad applications in healthcare and beyond.
摘要：
■从脑电图（EEG）信号中识别情绪是一项具有挑战性的任务，非线性，和大脑活动的非平稳特征。传统的方法往往无法捕捉到这些微妙的动态，而深度学习方法缺乏可解释性。在这项研究中,我们介绍了一种新颖的集成流形嵌入的三阶段方法，多级异质性复发分析(MHRA)，和集成学习来解决这些限制在基于脑电图的情绪识别。
■使用SJTU-SEEDIV数据库评估了所提出的方法。我们首先应用均匀流形近似和投影（UMAP）将62导联EEG信号的流形嵌入到低维空间中。然后，我们开发了MHRA来表征跨多个过渡水平的脑活动的复杂复发动力学。最后,我们采用基于树的集成学习方法对四种情绪进行分类(中性，悲伤,恐惧,快乐)基于提取的MHRA特征。
■我们的方法实现了高性能，准确度为0.7885，AUC为0.7552，优于同一数据集上的现有方法。此外,我们的方法提供了在不同情绪中最一致的识别性能.敏感性分析显示特定的MHRA指标与每种情绪密切相关，提供对潜在神经动力学的有价值的见解。
■这项研究提出了一种基于EEG的情感识别的新颖框架，该框架有效地捕获了复杂的非线性和非平稳的大脑活动动力学，同时保持了可解释性。所提出的方法为提高我们对情绪处理的理解和开发更可靠的情绪识别系统提供了巨大的潜力，并在医疗保健及其他领域具有广泛的应用。
■使用SJTU-SEEDIV数据库评估了所提出的方法。我们首先应用均匀流形近似和投影（UMAP）将62导联EEG信号的流形嵌入到低维空间中。然后，我们开发了MHRA来表征跨多个过渡水平的脑活动的复杂复发动力学。最后,我们采用基于树的集成学习方法对四种情绪进行分类(中性，悲伤,恐惧,快乐)基于提取的MHRA特征。
■我们的方法实现了高性能，准确度为0.7885，AUC为0.7552，优于同一数据集上的现有方法。此外,我们的方法提供了在不同情绪中最一致的识别性能.敏感性分析显示特定的MHRA指标与每种情绪密切相关，提供对潜在神经动力学的有价值的见解。
■这项研究提出了一种基于EEG的情感识别的新颖框架，该框架有效地捕获了复杂的非线性和非平稳的大脑活动动力学，同时保持了可解释性。所提出的方法为提高我们对情绪处理的理解和开发更可靠的情绪识别系统提供了巨大的潜力，并在医疗保健及其他领域具有广泛的应用。
