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从听觉自主感官午线响应 (ASMR) 识别情绪,使用多模体生理信号

Neha Gahlan¹, Divyashikha Sethia²

¹School of Cyber Security and Digital Forensics, National Forensic Sciences University - Gujarat Campus, Gandhinagar, Gujarat, Delhi, Gandhinagar, GJ, 382007, INDIA.

Physiological measurement

|January 8, 2026

概括

这项研究表明,生理信号和深度学习可以准确地分类由自主感应午线响应 (ASMR) 声音引起的快乐,悲伤和厌恶等情绪. 这些发现促进了心理健康应用的情绪识别.

科学领域:

神经科学和心理学神经科学和心理学
情感计算是一种情感计算.
生物医学工程生物医学工程

背景情况:

自主感官午线反应 (ASMR) 是一种感官现象,其特点是由特定的听觉刺激引发的刺痛感觉.
以前的研究主要集中在ASMR与放松和冷静等积极情绪的关联上.
这项研究调查了ASMR引起的更广泛的情绪,包括幸福,悲伤和厌恶.

研究的目的:

探索ASMR听觉刺激引发的情绪范围,超越放松.
研究多模体生理信号在识别ASMR诱导情绪方面的有效性.
应用深度学习模型来根据生理数据对这些情绪进行分类.

主要方法:

收集了23名参与者经历ASMR的多模体生理数据 (EEG,PPG,EDA).
将参与者暴露在各种ASMR听觉刺激中,旨在引起快乐,悲伤,平静和厌恶.
使用rmANOVA进行统计分析和人工神经网络 (ANN) 和卷积神经网络 (CNN) 进行情感分类,使用价值-激发-主导 (VAD) 维度.

主要成果:

统计分析显示,四种诱导情绪和中性状态之间的生理反应存在显著差异.
深度学习模型实现了高分类准确性:ANN为96.12%,CNN为74.25%,用于使用VAD维度的四种情绪.
结果表明,不同的ASMR诱导情绪具有不同的生理特征.

结论:

多模体生理信号与深度学习相结合,有效地分类ASMR诱导的情绪.
这项研究有助于情绪识别领域,特别是心理健康和治疗应用领域.
这些发现验证了使用生理学数据来客观地评估对听觉刺激的反应中的情绪.

关键词:

在ASMR中,ASMR是最重要的.情绪识别情绪识别多模态生理信号多模态生理信号统计分析统计分析可穿戴式传感器穿戴式传感器

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