Decoding preparatory movement state-based motor imagery with multi layer energy decoder.

ABSTRACT:

Background

Motor imagery (MI) is a widely used paradigm in brain-computer interface (BCI) research due to its potential applications in areas such as motor rehabilitation. As a purely cognitive process, MI produces low-amplitude, non-stationary EEG. Despite improving accuracies, cross-subject variability and limited generalization continue to motivate approaches that strengthen MI representations and enhance system robustness.

Methods

We designed a task-guided preparatory movement state-based motor imagery (PMS-MI) paradigm that elicits a brief motor preparatory state before MI and captures EEG features from both the preparation and imagery phases. To decode the features effectively, we introduced a multilayer energy decoder (MLED) that integrates graph signal processing (GS

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12821290 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature