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Learning CNN features from DE features for EEG-based emotion recognition
- Source :
- Pattern Analysis and Applications. 23:1323-1335
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
Abstract
- Recently, deep neural networks (DNNs) have shown the remarkable success of feature representations in computer vision, audio analysis, and natural language processing. Furthermore, DNNs have been used for electroencephalography (EEG) signal classification in recent studies on brain–computer interface. However, most works use one-dimensional EEG features to learn DNNs that ignores the local information within multichannel or multiple frequency bands in the EEG signals. In this paper, we propose a novel emotion recognition method using a convolutional neural network (CNN) while preventing the loss of local information. The proposed method consists of two parts. The first part generates topology-preserving differential entropy features while keeping the distance from the center electrode to other electrodes. The second part learns the proposed CNN to estimate three-class emotional states (positive, neutral, negative). We evaluate our work on SEED dataset, including 62-channel EEG signals recorded from 15 subjects. Our experimental results demonstrate that the proposed method achieved superior performance on SEED dataset with an average accuracy of 90.41% with the visualization of extracted features from the proposed CNN using t-SNE to show our representation outperforms the other representations based on standard features for EEG analysis. Besides, with the additional experiment on VIG dataset to estimate the vigilance of EEG dataset, we show the off-the-shelf availability of the proposed method.
- Subjects :
- medicine.diagnostic_test
business.industry
Computer science
020207 software engineering
Pattern recognition
02 engineering and technology
Electroencephalography
Convolutional neural network
Visualization
Differential entropy
ComputingMethodologies_PATTERNRECOGNITION
Signal classification
Artificial Intelligence
Audio analyzer
0202 electrical engineering, electronic engineering, information engineering
medicine
Deep neural networks
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
Emotion recognition
business
Subjects
Details
- ISSN :
- 1433755X and 14337541
- Volume :
- 23
- Database :
- OpenAIRE
- Journal :
- Pattern Analysis and Applications
- Accession number :
- edsair.doi...........1582bfa4ef76a0c3651cf3cea313475f