Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills

Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills

The Electroencephalography (EEG)-based motor imagery (MI) paradigm is one of the most studied technologies for Brain-Computer Interface (BCI) development. Still, the low Signal-to-Noise Ratio (SNR) poses a challenge when constructing EEG-based BCI systems. Moreover, the non-stationary and nonlinear...

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Bibliographic Details
Main Authors: Mateo Tobón-Henao, Andrés Álvarez-Meza, Germán Castellanos-Domínguez
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Sensors
Subjects:
Brain-Computer Interface
electroencephalography
motor imagery
artifact removal
functional connectivity
Online Access:https://www.mdpi.com/1424-8220/22/15/5771

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https://www.mdpi.com/1424-8220/22/15/5771

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