Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks
| Autor: | Jingxia Chen, Yufei Huang, Yuan-fang Zhang, Dongmei Jiang, P. W. Zhang, Z. J. Mao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Feature engineering
General Computer Science Computer science 02 engineering and technology Electroencephalography 01 natural sciences Convolutional neural network convolution neural network emotion recognition 0202 electrical engineering electronic engineering information engineering medicine General Materials Science EEG medicine.diagnostic_test business.industry combined features 010401 analytical chemistry General Engineering deep learning Pattern recognition Linear discriminant analysis 0104 chemical sciences Support vector machine ComputingMethodologies_PATTERNRECOGNITION Binary classification 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business Feature learning Classifier (UML) lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 7, Pp 44317-44328 (2019) |
| ISSN: | 2169-3536 |
| Popis: | In order to improve the accuracy of emotional recognition by end-to-end automatic learning of emotional features in spatial and temporal dimensions of electroencephalogram (EEG), an EEG emotional feature learning and classification method using deep convolution neural network (CNN) was proposed based on temporal features, frequential features, and their combinations of EEG signals in DEAP dataset. The shallow machine learning models including bagging tree (BT), support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian linear discriminant analysis (BLDA) models and deep CNN models were used to make emotional binary classification experiments on DEAP datasets in valence and arousal dimensions. The experimental results showed that the deep CNN models which require no feature engineering achieved the best recognition performance on temporal and frequency combined features in both valence and arousal dimensions, which is 3.58% higher than the performance of the best traditional BT classifier in valence dimension and 3.29% higher than that of BT classifier in arousal dimension. |
| Databáze: | OpenAIRE |
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