Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion
Authors: Dongyang Li, Chen Wei, Shiying Li, Jiachen Zou, Quanying Liu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this study, we present an end-to-end EEG-based visual reconstruction zero-shot framework... The experimental results indicate that our EEG-based visual zero-shot framework achieves SOTA performance in classification, retrieval and reconstruction... |
| Researcher Affiliation | Academia | Dongyang Li1 Chen Wei1 Shiying Li1 Jiachen Zou1 Quanying Liu1 1Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China {lidy2023, weic3}@mail.sustech.edu.cn liuqy@sustech.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/ncclab-sustech/EEG_Image_decode. |
| Open Datasets | Yes | We conducted our experiments on the THINGS-EEG dataset s training set [8, 6]. To verify the versatility of ATM for embedding electrophysiological data, we tested it on MEG data modality using the THINGS-MEG dataset [18]. |
| Dataset Splits | Yes | We splited the last batch of the original training set as the validation set and selected the best model based on the minimum validation loss over 40 epochs. For fairness, all models hyperparameters were kept consistent. In our study, we compared the performance of different encoders on the within-subject test set and cross-subject (leave-one-subject-out) test set (see Appendix H). |
| Hardware Specification | Yes | All experiments can be completed in a single NVIDIA RTX 4090 GPU. |
| Software Dependencies | No | The paper mentions the use of 'Adam optimizer [19]' but does not specify version numbers for any software libraries or dependencies (e.g., PyTorch, TensorFlow) required for replication. |
| Experiment Setup | Yes | We used the Adam optimizer [19] to train the across-subject model on a set of approximately 496,200 samples, and the within-subject model on a set of about 66,160 samples, with an initial learning rate of 3 \times 10 {-4} and batch sizes of 16 and 1024. Our initial temperature parameter was set to 0.07. |