Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems

A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if the model has been trained accurately, it should be able to identify a class and label it...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-05
Main Authors: Gallo, Alessandro, Manh Duong Phung
Format: Article
Language:English
Subjects:
Artificial neural networks
Electroencephalography
Human-computer interface
Imagery
Machine learning
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if the model has been trained accurately, it should be able to identify a class and label it accordingly. The CNN model will then be restored and used to try and identify the same class of motor imagery data using much smaller sampled data in an attempt to simulate live data.
ISSN:2331-8422