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29 October 2018 EEG-based emotion recognition by analyzing multi-frequency bands
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Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 108361V (2018) https://doi.org/10.1117/12.2326658
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China
Abstract
As an important research top in human-computer interaction, the relationship between electroencephalogram (EEG) and emotion recognition has attracted wide attention,however, the partial accuracy of emotion recognition is low. For improving the accuracy rate, original EEG signal is filtered to 5 bands (δ, θ, α, β, γ) then the Differential Entropy (DE), Power Spectral Density (PSD), Wavelet Entropy (WE) and Approximate Entropy (ApEn) are selected by feature extraction method. Finally, we select the features and use the support vector machine (SVM), KNN, Naive Bayes classifier and the neural network for classification learning. A large number of data generated by 62 channels is inconvenient to calculate, in this paper, we use SVM for training DE feature to get the higher accuracy with four different electrode placement methods. Through the study we found that the overall accuracy is generally higher than the accuracy of each frequency band. The high frequency band in emotional activities play a more important role than the low frequency band. Smaller band and channels can also achieve the high accuracy.
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Jiarui Zhang, Gang Wang, and Siyuan Wang "EEG-based emotion recognition by analyzing multi-frequency bands", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108361V (29 October 2018); https://doi.org/10.1117/12.2326658
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