Instance-Adaptive Graph for EEG Emotion Recognition
Authors: Tengfei Song, Suyuan Liu, Wenming Zheng, Yuan Zong, Zhen Cui2701-2708
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two widely-used EEG emotion recognition datasets are conducted to evaluate the proposed model and the experimental results show that our method achieves the state-of-the-art performance. |
| Researcher Affiliation | Academia | 1Key Laboratory of Child Development and Learning Science, Ministry of Education, Southeast University, Nanjing, China 2School of Information Science and Engineering, Southeast University, Nanjing, China 3School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China |
| Pseudocode | No | The paper describes its methods using text and mathematical equations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete statement or link regarding the availability of its source code. |
| Open Datasets | Yes | Experiments on two widely-used EEG emotion recognition datasets are conducted to evaluate the proposed model and the experimental results show that our method achieves the state-of-the-art performance. SEED The SJTU Emotion EEG Database (SEED) recorded 15 subjects EEG data... MPED The Multi-Modal Physiological Emotion Database (MPED) contains four types of physiological signals of 23 subjects... |
| Dataset Splits | Yes | For subject-dependent protocol, we use the first 9 trials of EEG data as training data and remaining 6 ones as testing data, and then the mean accuracy of 15 subjects is evaluated. For subject-independent protocol, we employ the leave-one-subject-out cross validation strategy. 21 trials of EEG data are served as training data and the rest 7 trials of EEG data are served as testing data. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used, such as GPU or CPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions 'Tensor Flow' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For the proposed graph convolution part, the number of EEG channels (n) is set to 62, the number of frequency bands (d) is set to 5, the order of graph convolution (K) is set to 8 and the transformed dimension (d ) is set to 32. For graph coarsening, original 62 nodes are clustered into 17 nodes. The dimensions of hidden state and memory cell in LSTM are both set to 64. In our loss function, the tradeoff parameters, i.e., α1, α2, α3, α4 and α5 are set to 10 4, 10 5, 10 5, 10 5 and 10 5, respectively. The learning rate is set to 0.001. |