What are the current advancements in emotion recognition techniques using EEG and HRV?
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Current advancements in emotion recognition techniques using EEG and HRV include innovative approaches like utilizing brain rhythm code features extracted from EEG signals . Researchers have proposed a hybrid CNN-LSTM model achieving high accuracies in valence and arousal dimensions . Moreover, a system combining advanced neural network models and eigenvalues related to emotional states has been developed for emotion recognition based on EEG and HRV signals, focusing on valence and arousal dimensions . Deep learning techniques, such as deep belief networks, convolutional neural networks, and recurrent neural networks, have been increasingly utilized to learn high-level feature representations for EEG emotion recognition, showcasing the potential for future advancements in this field .
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17 Jan 2023 | The current advancements include a real-time, two-dimensional emotion recognition system utilizing EEG and HRV signals, employing DenseNet neural network model and differential entropy for improved accuracy. |
| An innovative EEG-based emotion recognition technique utilizing brain rhythm code features from a single channel shows high accuracies (83-92%) with minimal data, advancing portable BCI devices. | |
17 Jan 2023 1 Citations | Advancements include real-time emotion recognition using EEG and HRV, employing DenseNet for machine learning, and focusing on valence and arousal dimensions for accurate emotion analysis. |
| Not addressed in the paper. |
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