EEG Motor Imagery Classification by Feature Extracted Deep 1D-CNN and Semi-Deep Fine-Tuning

EEG Motor Imagery Classification by Feature Extracted Deep 1D-CNN and Semi-Deep Fine-Tuning

The main goal of this paper is to introduce a Motor Imagery (MI) classification system for electroencephalography (EEG) that is extremely precise. To achieve this goal, we propose using a feature-extracted deep one-dimension (1D) convolutional neural network (CNN) which provides a model that can be...

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Bibliographic Details
Main Authors: Mohamad Taghizadeh, Fatemeh Vaez, Miad Faezipour
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Brain-computer interface
classification
deep learning
electroencephalogram
transfer learning
Online Access:https://ieeexplore.ieee.org/document/10601693/

Internet

https://ieeexplore.ieee.org/document/10601693/

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