ArticleHeliyon2024
Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network.Scientific reports · 2026Article
- Optimized hybrid Transformer-based model for EEG classification: application to wheelchair control.AIMS neuroscience · 2026Article
- A survey on data augmentation for EEG-based emotion recognition and cognitive workload decoding.Frontiers in neuroscience · 2026Review
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Authors and funding
6 authors.
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Abstract
At present, most methods to improve the accuracy of emotion recognition based on electroencephalogram (EEG) are achieved by means of increasing the number of channels and feature types. This is to use the big data to train the classification model but it also increases the code complexity and consumes a large amount of computer time. We propose a method of Ant Colony Optimization with Convolutional Neural Networks and Long Short-Term Memory (ACO-CNN-LSTM) which can attain the dynamic optimal channels for lightweight data. First, transform the time-domain EEG signal to the frequency domain by Fast Fourier Transform (FFT), and the Differential Entropy (DE) of the three frequency bands (
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Registered trials
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