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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書誌詳細

出版年:	Sensors
主要な著者:	Ilaria Siviero, Gloria Menegaz, Silvia Francesca Storti
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2023-08-01
主題:	functional brain connectivity motor-imagery brain–computer interface translation-invariant features scattering convolution network feature fusion multiclass classification
オンライン･アクセス:	https://www.mdpi.com/1424-8220/23/17/7520