Active Selection of Source Patients in Transfer Learning for Epileptic Seizure Detection Using Riemannian Manifold
Author:
Affiliation:
1. Tokyo University of Agriculture and Technology,Graduate School of Engineering,Tokyo,Japan
Funder
Japan Science and Technology Agency
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10095272.pdf?arnumber=10095272
Reference22 articles.
1. Selective Cross-Subject Transfer Learning Based on Riemannian Tangent Space for Motor Imagery Brain-Computer Interface
2. Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition
3. Multiband tangent space mapping and feature selection for classification of EEG during motor imagery
4. Dimensionality reduction of sample covariance matrices by graph fourier transform for motor imagery brain-machine interface
5. Anomaly detection
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1. Cross-Subject Seizure Detection via Unsupervised Domain-Adaptation;International Journal of Neural Systems;2024-08-09
2. Detection of Epileptic Seizures in Long Eeg Recordings Using an Anomaly Detector with Artifact Rejection;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
3. BNMTrans: A Brain Network Sequence-Driven Manifold-Based Transformer for Cognitive Impairment Detection Using EEG;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
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