Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Local-Ascending-Global Learning Strategy for Brain-Computer Interface
Authors: Dongrui Gao, Haokai Zhang, Pengrui Li, Tian Tang, Shihong Liu, Zhihong Zhou, Shaofei Ying, Ye Zhu, Yongqing Zhang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed LAG strategy is validated using datasets related to fatigue (SEED-VIG), emotion (SEED-IV), and motor imagery (BCI C IV 2a). The results demonstrate the generalizability of LAG, achieving satisfactory outcomes in independent-subject experiments across all three datasets. |
| Researcher Affiliation | Academia | 1School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China 2 School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China |
| Pseudocode | Yes | Algorithm 1: Training Stage Input: Train set G = {BM} Output: {ypred, Loss} |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code availability for the described methodology. |
| Open Datasets | Yes | This paper presents validation experiments conducted on three EEG datasets (SEED-VIG, SEED-IV, BCI C IV 2a) each associated with distinct cognitive tasks (Gao et al. 2023a; Peng et al. 2023; Zhang et al. 2019). |
| Dataset Splits | No | The paper mentions 'independent-subject experiments' and 'cross-subject comparison results' but does not specify exact split percentages or sample counts for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions the 'Adam optimizer' but does not provide specific version numbers for any software dependencies or frameworks used. |
| Experiment Setup | Yes | The graph convolution order is set to 2, and a dropout rate of 0.5 is applied. Model parameters are optimized using the Adam optimizer, with a learning rate search range of [1e-3, 1e-1] and an L2 regularization search range of [5e-3, 3e-1]. |