Electrodermal Activity for Stress Identification

Dataset Description The "Electrodermal Activity for Stress Identification" dataset is associated with the study "Deep Support Vector Machines for the Identification of Stress Condition from Electrodermal Activity" and provides physiological data used to classify stress and calm states based on electrodermal activity (EDA) signals. Dataset Details Source: The data was collected from 147 volunteers who participated in an experiment designed to induce stress and calm conditions through audiovisual stimuli exposure. Recording Device: The Empatica E4 wristband was used, a commercially available and validated device for electrodermal activity measurement. Included Variables: Raw EDA signals: Continuous recordings of electrodermal activity measured in microsiemens (µS). Extracted Features: Processed data in the time domain, frequency domain, and morphological analysis. State Labels: Each data segment is classified as stress or calm, based on the experimental condition. Time Stamps: Time markers for each recording in seconds, tracking the signal evolution during stimulus exposure. File Format: The dataset is available in CSV and MAT formats, making it compatible with Python, MATLAB, and other data science tools. Usage and Applications This dataset is useful for research in computational neuroscience, machine learning, psychophysiology, and digital health. It can be applied to: Training and validating stress classification models using machine learning algorithms such as SVM, neural networks, and deep learning. Analyzing physiological stress patterns in controlled environments. Developing biofeedback systems and mental well-being applications for real-time stress detection. Open Access and Availability The dataset has been published in open access on the Zenodo repository, allowing for reuse and replicability in future research. License This dataset is available under a Creative Commons (CC-BY 4.0) license, permitting usage, modification, and redistribution as long as proper citation is given to the original source.数据集说明 本“用于应激识别的皮肤电活动（Electrodermal Activity, EDA）”数据集关联研究论文《基于皮肤电活动的应激状态识别的深度支持向量机》，提供基于皮肤电活动信号分类应激与平静状态的生理数据。 数据集详情 数据来源：数据采集自147名参与实验的志愿者，该实验通过视听刺激诱发应激与平静状态。 记录设备：采用Empatica E4腕带，这是一款商用且经过验证的皮肤电活动测量设备。 包含变量： - 原始EDA信号：以微西门子（µS）为单位的皮肤电活动连续记录。 - 提取特征：经过时域、频域处理及形态学分析的预处理数据。 - 状态标签：每个数据片段均根据实验条件被标记为应激或平静状态。 - 时间戳：以秒为单位的每条记录的时间标记，用于追踪刺激呈现期间的信号演变。 文件格式：本数据集提供CSV与MAT两种格式，可兼容Python、MATLAB及其他数据科学工具。 应用场景 本数据集可用于计算神经科学、机器学习、心理生理学及数字健康领域的研究，可应用于： 1. 采用支持向量机（Support Vector Machine, SVM）、神经网络与深度学习等机器学习算法训练并验证应激分类模型； 2. 分析受控环境下的生理应激模式； 3. 开发用于实时应激检测的生物反馈系统与心理健康应用。 开放获取与可用性 本数据集已在Zenodo知识库以开放获取形式发布，支持后续研究的复用与可重复性。 授权协议 本数据集采用知识共享（CC-BY 4.0）许可协议发布，允许在正确标注原始来源的前提下进行使用、修改与再分发。