Dataset of Multi-Modal Physiological Signals (fNIRS, EEG, ECG, EMG) Recorded Across Different Emotional States

This dataset comprises synchronized multi-modal physiological recordings—functional Near-Infrared Spectroscopy (fNIRS), Electroencephalography (EEG), Electrocardiography (ECG), and Electromyography (EMG)—collected from 16 participants exposed to emotion-eliciting video stimuli. It includes raw signals, event markers, and Python scripts for data import and preprocessing. Special emphasis is placed on fNIRS, which, though less common in affective computing, provides valuable hemodynamic insights that complement electrical signals from EEG, ECG, and EMG. The dataset is structured to facilitate reproducibility and ease of integration across platforms. It aims to support research in emotion recognition, multimodal data fusion, and machine learning applications in emotion-aware and human-centered systems.

本数据集包含同步多模态生理记录数据，涵盖功能性近红外光谱（functional Near-Infrared Spectroscopy, fNIRS）、脑电图（Electroencephalography, EEG）、心电图（Electrocardiography, ECG）以及肌电图（Electromyography, EMG），采集自16名暴露于情绪诱发视频刺激下的受试者。数据集包含原始信号、事件标记，以及用于数据导入与预处理的Python脚本。本数据集特别侧重fNIRS：尽管其在情感计算领域的应用尚不普及，但该技术可提供极具价值的血液动力学观测结果，能够补充脑电图、心电图与肌电图所采集的电生理信号。该数据集的架构设计旨在提升研究可重复性，并便于跨平台集成。本数据集旨在为情绪识别、多模态数据融合，以及情感感知与以人为中心的系统中的机器学习应用相关研究提供支撑。

搜集汇总

背景与挑战

背景概述

该数据集包含16名参与者在情绪激发视频刺激下的多模态生理信号（fNIRS、EEG、ECG、EMG）同步记录，特别强调了fNIRS的应用。数据集旨在支持情感识别、多模态数据融合和机器学习在情感感知系统中的应用研究。