SADT dataset, SEED-VIG dataset
The SADT and SEED‑VIG datasets are used for cross‑dataset driver fatigue recognition tasks via EEG data. These processed datasets can be used to test and train models to improve driver fatigue detection accuracy.
Dataset description and usage context
EEG‑based Cross‑dataset Driver Drowsiness Recognition with an Entropy‑Optimization Network
Dataset Overview
The dataset is used for EEG‑based cross‑dataset driver drowsiness recognition tasks. It includes two publicly available datasets:
-
SADT dataset:
-
SEED‑VIG dataset:
Model Description
The proposed model, Entropy‑Optimization Network (EON), adopts a two‑step strategy to separate unlabeled target‑domain data. First, samples are pushed from the source domain toward the decision boundary, then a self‑training framework gradually separates them (entropy reduction), fully exploiting latent patterns in the unlabeled data.
Experimental Results
The method was tested on domain‑adaptation tasks, achieving 2‑class recognition accuracies of 89.2% and 77.6%, outperforming baseline methods.
Pair the dataset with AI analysis and content workflows.
Once the source passes your review, move straight into summarization, transformation, report drafting, or presentation generation with the JuheAI toolchain.