An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training.

ABSTRACT: Brain-computer interface (BCI) has developed rapidly over the past two decades, mainly due to advancements in machine learning. Subjects must learn to modulate their brain activities to ensure a successful BCI. Feedback training is a practical approach to this learning process; however, the commonly used classifier-dependent approaches have inherent limitations such as the need for calibration and a lack of continuous feedback over long periods of time. This paper proposes an online data visualization feedback protocol that intuitively reflects the EEG distribution in Riemannian geometry in real time. Rather than learning a hyperplane, the Riemannian geometry formulation allows iterative learning of prototypical covariance matrices that are translated into visualized feedback through diffu

SUBMITTER: Duan X 

PROVIDER: S-EPMC8215169 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature