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Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition
| Content Provider | MDPI |
|---|---|
| Author | Li, Qi Liu, Yunqing Shang, Yujie Zhang, Qiong Yan, Fei |
| Copyright Year | 2022 |
| Description | Recently, emotional electroencephalography (EEG) has been of great importance in brain–computer interfaces, and it is more urgent to realize automatic emotion recognition. The EEG signal has the disadvantages of being non-smooth, non-linear, stochastic, and susceptible to background noise. Additionally, EEG signal processing network models have the disadvantages of a large number of parameters and long training time. To address the above issues, a novel model is presented in this paper. Initially, a deep sparse autoencoder network (DSAE) was used to remove redundant information from the EEG signal and reconstruct its underlying features. Further, combining a convolutional neural network (CNN) with long short-term memory (LSTM) can extract relevant features from task-related features, mine the correlation between the 32 channels of the EEG signal, and integrate contextual information from these frames. The proposed DSAE + CNN + LSTM (DCRNN) model was experimented with on the public dataset DEAP. The classification accuracies of valence and arousal reached 76.70% and 81.43%, respectively. Meanwhile, we conducted experiments with other comparative methods to further demonstrate the effectiveness of the DCRNN method. |
| Starting Page | 1187 |
| e-ISSN | 10994300 |
| DOI | 10.3390/e24091187 |
| Journal | Entropy |
| Issue Number | 9 |
| Volume Number | 24 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-25 |
| Access Restriction | Open |
| Subject Keyword | Entropy Industrial Engineering Eeg Emotion Recognition Deep Sparse Autoencoder Cnn Lstm |
| Content Type | Text |
| Resource Type | Article |
