Reducing the Dimensionality of SPD Matrices with Neural Networks in BCI

Reducing the Dimensionality of SPD Matrices with Neural Networks in BCI

In brain–computer interface (BCI)-based motor imagery, the symmetric positive definite (SPD) covariance matrices of electroencephalogram (EEG) signals with discriminative information features lie on a Riemannian manifold, which is currently attracting increasing attention. Under a Riemannian manifol...

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Bibliographic Details
Main Authors: Zhen Peng, Hongyi Li, Di Zhao, Chengwei Pan
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Mathematics
Subjects:
brain–computer interface
Riemannian geometry
SPD manifolds
non-linear dimensionality reduction
neural networks
Online Access:https://www.mdpi.com/2227-7390/11/7/1570

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