A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet

A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet

Multiclass motor imagery classification is essential for brain–computer interface systems such as prosthetic arms. The compressive sensing of EEG helps classify brain signals in real-time, which is necessary for a BCI system. However, compressive sensing is limited, despite its flexibility and data...

Full description

Bibliographic Details
Main Authors: Harshini Gangapuram, Vidya Manian
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Signals
Subjects:
compressive sensing
sparse representation
motor imagery EEG classification
1D-CNN
residual networks
Online Access:https://www.mdpi.com/2624-6120/4/1/13

Internet

https://www.mdpi.com/2624-6120/4/1/13

Similar Items