Emotion Recognition from EEG Signals Using Recurrent Neural Networks

The application of electroencephalogram (EEG)-based emotion recognition (ER) to the brain–computer interface (BCI) has become increasingly popular over the past decade. Emotion recognition systems involve pre-processing and feature extraction, followed by classification. Deep learning has recently b...

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Veröffentlicht in:Electronics (Basel) 2022-08, Vol.11 (15), p.2387
Hauptverfasser: Chowdary, M. Kalpana, Anitha, J., Hemanth, D. Jude
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Anitha, J.
Hemanth, D. Jude
description The application of electroencephalogram (EEG)-based emotion recognition (ER) to the brain–computer interface (BCI) has become increasingly popular over the past decade. Emotion recognition systems involve pre-processing and feature extraction, followed by classification. Deep learning has recently been used to classify emotions in BCI systems, and the results have been improved when compared to classic classification approaches. The main objective of this study is to classify the emotions from electroencephalogram signals using variant recurrent neural network architectures. Three architectures are used in this work for the recognition of emotions using EEG signals: RNN (recurrent neural network), LSTM (long short-term memory network), and GRU (gated recurrent unit). The efficiency of these networks, in terms of performance measures was confirmed by experimental data. The experiment was conducted by using the EEG Brain Wave Dataset: Feeling Emotions, and achieved an average accuracy of 95% for RNN, 97% for LSTM, and 96% for GRU for emotion detection problems.
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subjects Algorithms
Classification
Computer architecture
Deep learning
Electroencephalography
Emotion recognition
Emotions
Feature extraction
Human-computer interface
Machine learning
Machine translation
Memory
Neural networks
Recurrent neural networks
title Emotion Recognition from EEG Signals Using Recurrent Neural Networks
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