Search Results for author:
Found 3 papers, 2 papers with code
BDAN: Mitigating Temporal Difference Across Electrodes in Cross-Subject Motor Imagery Classification via Generative Bridging Domain
Based on the presented issue, a novel bridging domain adaptation network (BDAN) is proposed, aiming to minimise the data distribution difference across sessions in the aspect of the electrode, thus improving and enhancing model performance.
EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification
The results demonstrate the superior feature extraction ability of the proposed EEGProgress, with an average increase of 4. 02% compared to other CNN-based EEG classification models under both cross-subject and within-subject scenarios.
Spatial Variation Generation Algorithm for Motor Imagery Data Augmentation: Increasing the Density of Sample Vicinity
Moreover, SVG generates a uniform distribution and stabilizes the training process of models.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
