FCEEG: federated learning-based seizure diagnosis through electroencephalogram (EEG) analysis
Electroencephalography (EEG) signals are crucial for seizure diagnosis. The data provides detailed insights into brain activity which aids in epilepsy management. Artificial intelligence (AI) and deep learning are widely employed in the analysis of EEG signals to achieve promising classification per...
Saved in:
| Main Authors: | Zheng You Lim, Ying Han Pang, Shih Yin Ooi, Sarmela Raja Sekaran, Yee Jian Chew |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2025.2547636 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Automated Seizure Detection through EEG Analysis and Deep Learning Technique
by: Srinivas Nowduri, et al.
Published: (2024-06-01) -
A companion to the preclinical common data elements for phenotyping seizures and epilepsy in rodent models. A report of the TASK3‐WG1C: Phenotyping working group of the ILAE/AES joint translational task force
by: Melissa Barker‐Haliski, et al.
Published: (2025-08-01) -
Communication-Balancing Threshold for Event-Triggered Federated Learning
by: Juhyeong Yoon, et al.
Published: (2025-01-01) -
Automated sleep staging from single-channel electroencephalogram using hybrid neural network with manual features and attention
by: Qingyun Wan, et al.
Published: (2025-08-01) -
FedWT: Federated Learning with Minimum Spanning Tree-based Weighted Tree Aggregation for UAV networks
by: Geonhui Kim, et al.
Published: (2025-04-01)