Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain–Computer Interface Performance

Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain–Computer Interface Performance

(1) Background: in the field of motor-imagery brain–computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which le...

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Bibliographic Details
Main Authors:	Ilaria Siviero, Gloria Menegaz, Silvia Francesca Storti
Format:	Article
Language:	English
Published:	MDPI AG 2023-08-01
Series:	Sensors
Subjects:	functional brain connectivity motor-imagery brain–computer interface translation-invariant features scattering convolution network feature fusion multiclass classification
Online Access:	https://www.mdpi.com/1424-8220/23/17/7520

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