Neurophysiological predictors of deep learning based unilateral upper limb motor imagery classification.

ABSTRACT:

Introduction

Motor imagery-based brain-computer interfaces (BCIs) are a technique for decoding and classifying the intention of motor execution, solely based on imagined (rather than executed) movements. Although deep learning techniques have increased the potential of BCIs, the complexity of decoding unilateral upper limb motor imagery remains challenging. To understand whether neurophysiological features, which are directly related to neural mechanisms of motor imagery, might influence classification accuracy, most studies have largely leveraged traditional machine learning frameworks, leaving deep learning-based techniques underexplored.

Methods

In this work, three different deep learning models from the literature (EEGNet, FBCNet, NFEEG) and two common spatial pattern-ba

SUBMITTER: Sonntag J 

PROVIDER: S-EPMC12272612 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature