An Efficient Bidirectional LSTM-Based Deep Neural Network for Automatic Emotion Recognition Using EEG Signal-Reference-Cited by-同舟云学术

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An Efficient Bidirectional LSTM-Based Deep Neural Network for Automatic Emotion Recognition Using EEG Signal

Published:2022-12-21 Issue: Volume: Page:
ISSN:
Container-title:2022 12th International Conference on Electrical and Computer Engineering (ICECE)
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Author:

Mahmud Md. Sultan¹,Saha Oishy¹,Fattah Shaikh Anowarul¹

Affiliation:

1. Bangladesh University of Engineering and Technology (BUET),Department of Electrical and Electronic Engineering (EEE),Dhaka,Bangladesh,1205

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10088438/10088464/10088864.pdf?arnumber=10088864

Reference22 articles.

1. Subject independent emotion recognition using EEG signals employing attention driven neural networks;rajpoot;Biomedical Signal Processing and Control,2022

2. Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks

3. Emotion Recognition from Multi-Channel EEG through Parallel Convolutional Recurrent Neural Network

4. EEG Channel Correlation Based Model for Emotion Recognition

5. DEAP: A Database for Emotion Analysis ;Using Physiological Signals

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Automatic Emotion Recognition from EEG Signal Utilizing Wavelet Packet Node Reconstruction and a CNN Classifier;2023 26th International Conference on Computer and Information Technology (ICCIT);2023-12-13

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