基于过渡网络的情感识别皮电活动信号分析

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2024-08-01 DOI:10.1016/j.irbm.2024.100849
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引用次数: 0

摘要

情绪评估在了解和提高人类生活的各个方面(从心理健康、社会交往到决策过程)方面发挥着至关重要的作用。皮电活动(EDA)对交感神经系统活动高度敏感,因此被广泛用于情绪评估。虽然现有许多基于 EDA 的情绪评估方法,但它们往往无法捕捉 EDA 的动态非线性变化和时变特征。这些局限性阻碍了它们根据 "唤醒"(Arousal)和 "情绪"(Valence)维度对情绪状态进行准确分类的有效性。本研究旨在通过引入过渡网络分析(TNA)作为基于 EDA 的情绪评估的新方法来解决这些不足。为了探索 EDA 中的动态非线性变化及其对 "唤醒 "和 "情感 "维度分类的影响,我们将 EDA 数据分解为相位和强直成分。相位信息通过过渡网络来表示。我们从过渡网络中提取了七个特征。这些特征随后被用于使用四种不同的机器学习分类器进行分类:逻辑回归、多层感知器、随机森林和支持向量机(SVM)。每种分类器的性能都是通过 "留空-主体-淘汰 "交叉验证进行评估的。研究评估了这些分类器在表征情感维度方面的性能。研究结果表明,在过渡网络特征中,度中心性(Degree Centrality)和接近中心性(Closeness Centrality)存在显著差异,能够有效描述 "唤醒"(Arousal)和 "情绪"(Valence)维度。在分类器中,SVM 在 "唤醒 "和 "情感 "分类方面的 F1 分数分别达到了 71% 和 72%。这项研究具有重要意义,因为它不仅加深了我们对 EDA 非线性动态的理解,还展示了 TNA 在解决基于 EDA 的情绪评估现有技术的局限性方面的潜力。研究结果为在自然环境中开发可穿戴的情感发展监测设备提供了令人兴奋的机遇,弥补了情感计算领域的一个重要空白。此外,这项研究还强调了认识到当前基于 EDA 的情绪评估方法的局限性的重要性,并为追求更准确、更全面的情绪状态分类提出了一条创新之路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Transition Network-Based Analysis of Electrodermal Activity Signals for Emotion Recognition

Transition Network-Based Analysis of Electrodermal Activity Signals for Emotion Recognition

Background

Emotion assessment plays a vital role in understanding and enhancing various aspects of human life, from mental well-being and social interactions to decision-making processes. Electrodermal Activity (EDA) is widely used for emotion assessment, as it is highly sensitive to sympathetic nervous system activity. While numerous existing approaches are available for EDA-based emotion assessment, they often fall short in capturing the dynamic non-linear variations and time-varying characteristics of EDA. These limitations hinder their effectiveness in accurately classifying emotional states along the Arousal and Valence dimensions. This study aims to address these shortcomings by introducing Transition Network Analysis (TNA) as a novel approach to EDA-based emotion assessment.

Methods

To explore the dynamic non-linear variations in EDA and their impact on the classification of Arousal and Valence dimensions, we decomposed EDA data into its phasic and tonic components. The phasic information is represented over a transition network. From the transition network, we extracted seven features. These features were subsequently used for classification purposes employing four different machine learning classifiers: logistic regression, multi-layer perceptron, random forest, and support vector machine (SVM). The performance of each classifier was evaluated using Leave-One-Subject-Out cross-validation. The study evaluated the performance of these classifiers in characterizing emotional dimensions.

Results

The results of this research reveal significant variations in Degree Centrality and Closeness Centrality within the transition network features, enabling effective characterization of Arousal and Valence dimensions. Among the classifiers, the SVM achieved F1 scores of 71% and 72% for Arousal and Valence classification, respectively.

Significance

This study holds significant implications as it not only enhances our understanding of EDA's non-linear dynamics but also demonstrates the potential of TNA in addressing the limitations of existing techniques for EDA-based emotion assessment. The findings open exciting opportunities for the advancement of wearable EDA monitoring devices in naturalistic settings, bridging a critical gap in the field of affective computing. Furthermore, this research underlines the importance of recognizing the limitations in current EDA-based emotion assessment methods and suggests an innovative path forward in the pursuit of more accurate and comprehensive emotional state classification.

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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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