Name
Title | ||
|---|---|---|
CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From the State-of-The-Art to DynamicNet |
Abstract | ||
|---|---|---|
The accurate detection of motor imagery (MI) from electroencephalography (EEG) is a fundamental, as well as challenging, task to provide reliable control of robotic devices to support people suffering from neuro-motor impairments, e.g., in brain-computer interface (BCI) applications. Recently, deep learning approaches have been able to extract subject-independent features from EEG, to cope with it... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CIBCB49929.2021.9562821 | 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) |
Keywords | DocType | ISBN |
Deep learning,Image coding,Tools,Brain modeling,Feature extraction,Electroencephalography,Reliability | Conference | 978-1-6654-0112-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alberto Zancanaro | 1 | 0 | 0.34 |
| Giulia Cisotto | 2 | 1 | 1.04 |
| João Ruivo Paulo | 3 | 0 | 0.68 |
| Gabriel Pires | 4 | 0 | 0.68 |
| Urbano J. Nunes | 5 | 0 | 0.34 |