Cargando…
SPD-CNN: A plain CNN-based model using the symmetric positive definite matrices for cross-subject EEG classification with meta-transfer-learning
The electroencephalography (EEG) signals are easily contaminated by various artifacts and noise, which induces a domain shift in each subject and significant pattern variability among different subjects. Therefore, it hinders the improvement of EEG classification accuracy in the cross-subject learni...
Autores principales: | Chen, Lezhi, Yu, Zhuliang, Yang, Jian |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9383414/ https://www.ncbi.nlm.nih.gov/pubmed/35990886 http://dx.doi.org/10.3389/fnbot.2022.958052 |
Ejemplares similares
-
Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models
por: Yang, Chia-Yen, et al.
Publicado: (2023) -
EEG-based emotion recognition using hybrid CNN and LSTM classification
por: Chakravarthi, Bhuvaneshwari, et al.
Publicado: (2022) -
EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM
por: Mughal, Nabeeha Ehsan, et al.
Publicado: (2022) -
Epileptic Seizure Detection Based on EEG Signals and CNN
por: Zhou, Mengni, et al.
Publicado: (2018) -
A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals
por: Lun, Xiangmin, et al.
Publicado: (2020)