Online ECG Emotion Recognition for Unknown Subjects via Hypergraph-Based Transfer Learning
Authors: Yalan Ye, Tongjie Pan, Qianhe Meng, Jingjing Li, Li Lu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on the Amigos dataset validate the superiority of the proposed method. Extensive experiments on the Amigos dataset demonstrate the validity and performance of our HOTL method in online emotion recognition. |
| Researcher Affiliation | Academia | Yalan Ye , Tongjie Pan , Qianhe Meng , Jingjing Li and Li Lu University of Electronic Science and Technology of China yalanye@uestc.edu.cn, tongjiepan@foxmail.com, lijin117@yeah.net, qianhe@std.uestc.edu.cn Luli2009@uestc.edu.cn |
| Pseudocode | No | The paper describes the method's steps in text and equations, but it does not include any explicitly labeled "Pseudocode" or "Algorithm" blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | AMIGOS is a publicly available database [Correa et al., 2018] |
| Dataset Splits | Yes | To evaluate our proposed method in online setting, we use the leave one subject out cross-validation (LOSO), i.e., one subject is select as the unknown subject and the rest subjects are in training process each experiment. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., "Python 3.8", "PyTorch 1.9"). It mentions using "Resnet" but this is a model architecture, not a software dependency with a version. |
| Experiment Setup | Yes | The ECG signal was then filtered with a band-pass filter between 0.5 and 30 Hz. Then each ECG signal is divided into 20-s segments. The best results of our model is in µ = 1 and λ = 5. |