Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter
Electroencephalogram (EEG) modeling in brain-computer interface (BCI) provides a theoretical foundation for its development. However, limited by the lack of guidelines in model parameter selection and the inability to obtain personal tissue information in practice, EEG modeling in BCI is mainly focu...
Enregistré dans:
Auteurs principaux: | Xiaodong Zhang, Zhufeng Lu, Teng Zhang, Hanzhe Li, Yachun Wang, Qing Tao |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Frontiers Media S.A.
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/8044c90a3f8642e69b806e9bd304e20d |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
The classification of EEG-based wink signals: A CWT-Transfer Learning pipeline
par: Jothi Letchumy Mahendra Kumar, et autres
Publié: (2021) -
Control de movimiento robótico con detección cognitiva y facial mediante Emotiv EEG
par: Monge Lay,Sebastián, et autres
Publié: (2015) -
Classification of EEG Signals using Fast Fourier Transform (FFT) and Adaptive Neuro Fuzzy Inference System (ANFIS)
par: Suwanto Suwanto, et autres
Publié: (2019) -
Contextual Imputation With Missing Sequence of EEG Signals Using Generative Adversarial Networks
par: Woonghee Lee, et autres
Publié: (2021) -
Optical Properties of GaN-Based Green Light-Emitting Diodes Influenced by Low-Temperature p-GaN Layer
par: Jianfei Li, et autres
Publié: (2021)