An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model

Electroencephalography (EEG) is a non-invasive technique with high temporal resolution and cost-effective, portable, and easy-to-use features. Motor imagery EEG (MI-EEG) data classification is one of the key applications within brain–computer interface (BCI) systems, utilizing EEG signals from motor...

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Bibliographic Details
Main Authors:	Rajesh Kannan Megalingam, Kariparambil Sudheesh Sankardas, Sakthiprasad Kuttankulangara Manoharan
Format:	Article
Language:	English
Published:	MDPI AG 2024-11-01
Series:	Sensors
Subjects:	brain–computer interface (BCI) motor imagery-electroencephalography (MI-EEG) motion artifacts convolutional neural network (CNN) quadriplegics empirical error model
Online Access:	https://www.mdpi.com/1424-8220/24/23/7690
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An Empirical Model-Based Algorithm for Removing Motion-Caused Artifacts in Motor Imagery EEG Data for Classification Using an Optimized CNN Model

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