A wavelet-based approach to emotion classification using EDA signals

作者：

Highlights：

• Emotion classification based on the biological signals acquired from human subjects.

• Evaluating the capability of wearable assistive device (e.g., Q-sensors) in recognizing emotions.

• Wavelet-based feature extraction and time-frequency analysis of electrodermal activity (EDA) signals.

摘要

•Emotion classification based on the biological signals acquired from human subjects.•Evaluating the capability of wearable assistive device (e.g., Q-sensors) in recognizing emotions.•Wavelet-based feature extraction and time-frequency analysis of electrodermal activity (EDA) signals.

论文关键词：Emotion classification,Feature extraction,Time-frequency analysis,Wearable device,Eletrodermal activity

论文评审过程：Received 18 August 2017, Revised 15 May 2018, Accepted 7 June 2018, Available online 18 June 2018, Version of Record 26 June 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.06.014