Implementation of an Automatic EEG Feature Extraction with Gated Recurrent Neural Network for Emotion Recognition-Reference-Cited by-同舟云学术

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Implementation of an Automatic EEG Feature Extraction with Gated Recurrent Neural Network for Emotion Recognition

Published:2023 Issue: Volume: Page:133-150
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
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Author:

Immanuel Rajeswari Rajesh,Sangeetha S. K. B.

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-7169-3_13

Reference26 articles.

1. Rahman A, Hossain F, Hossain M, Ahmmed R (2020) Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal. Egypt Inf J 21(1):23–35. ISSN 1110-8665https://doi.org/10.1016/j.eij.2019.10.002

2. Mohammad A, Siddiqui F, Afshar Alam M (2021) Feature extraction from EEG signals: a deep learning perspective. In: 11th international conference on cloud computing, data science & engineering (Confluence), pp 757–760. https://doi.org/10.1109/Confluence51648.2021.9377108

3. Gao Q, Wang C-H, Wang Z, Song X-L, Dong E-Z, Song Y (2020) EEG based emotion recognition using fusion feature extraction method. Multimed Tools Appl 79:27057–27074 (2020). https://doi.org/10.1007/s11042-020-09354-y

4. Islam MR, Barua S, Ahmed MU, Begum S, Di Flumeri G (2019) Deep learning for automatic EEG feature extraction: an application in drivers’ mental workload classification. In: Longo L, Leva M (eds) Human mental workload: models and applications. H-WORKLOAD 2019. Communications in computer and information science, vol 1107. Springer, Cham. https://doi.org/10.1007/978-3-030-32423-0_8

5. Khare SK, Bajaj V (2021) Time–frequency representation and convolutional neural network-based emotion recognition. IEEE Trans Neural Netw Learn Syst 32(7):2901–2909 (2021). https://doi.org/10.1109/TNNLS.2020.3008938

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3. Emotion recognition in EEG signals using deep learning methods: A review;Computers in Biology and Medicine;2023-10

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