AutoEncoder Filter Bank Common Spatial Patterns to Decode Motor Imagery From EEG
Author:
Affiliation:
1. DICEAM Department, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy
2. College of Engineering and Departments of Neuroscience and Biomedical Informatics, The Ohio State University, Columbus, OH, USA
Funder
iCARE Project
Regione Calabria
European Community Resources of FESR
FSE, of Italy and of Calabria
PON
COGITO
Programma Operativo Nazionale
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics
Link
http://xplorestaging.ieee.org/ielx7/6221020/10116032/10041819.pdf?arnumber=10041819
Reference50 articles.
1. A Long Short-Term Memory Autoencoder Approach for EEG Motor Imagery Classification
2. Motor Intention Decoding from the Upper Limb by Graph Convolutional Network Based on Functional Connectivity
3. An Accurate EEGNet-based Motor-Imagery Brain–Computer Interface for Low-Power Edge Computing
4. A New Channel Selection Method using Autoencoder for Motor Imagery based Brain Computer Interface
5. Continuous sensorimotor rhythm based brain computer interface learning in a large population;stieger;Data Science Journal,2021
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