A simplified PPG based approach for automated recognition of five distinct emotional states
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16744-5.pdf
Reference35 articles.
1. Shin D, Shin D, Shin D (2017) Development of emotion recognition interface using complex EEG/ECG bio-signal for interactive contents. Multimed Tools Appl 76(9):11449–11470. https://doi.org/10.1007/s11042-016-4203-7
2. Yoo G, Seo S, Hong S, Kim H (2018) Emotion extraction based on multi bio-signal using back-propagation neural network. Multimed Tools Appl 77(4):4925–4937. https://doi.org/10.1007/s11042-016-4213-5
3. Dang WD, Lv DM, Li RM, Rui LG, Yang ZY, Ma C, Gao ZK (2022) Multilayer network-based cnn model for emotion recognition. Int J Bifurcation Chaos. 32(1). https://doi.org/10.1142/S0218127422500110
4. Wagh KP, Vasanth K (2022) Performance evaluation of multi-channel electroencephalogram signal (EEG) based time frequency analysis for human emotion recognition. Biomed Signal Process Control 78:103966. https://doi.org/10.1016/j.bspc.2022.103966.6
5. Zhang Y, Zhang S, Ji X (2018) EEG-based classification of emotions using empirical mode decomposition and autoregressive model. Multimed Tools Appl 77(20):26697–26710. https://doi.org/10.1007/s11042-018-5885-9
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