A novel paradigm for fast training data generation in asynchronous movement-based BCIs.

ABSTRACT:

Introduction

Movement-based brain-computer interfaces (BCIs) utilize brain activity generated during executed or attempted movement to provide control over applications. By relying on natural movement processes, these BCIs offer a more intuitive control compared to other BCI systems. However, non-invasive movement-based BCIs utilizing electroencephalographic (EEG) signals usually require large amounts of training data to achieve suitable accuracy in the detection of movement intent. Additionally, patients with movement impairments require cue-based paradigms to indicate the start of a movement-related task. Such paradigms tend to introduce long delays between trials, thereby extending training times. To address this, we propose a novel experimental paradigm that enables the collect

SUBMITTER: Crell MR 

PROVIDER: S-EPMC11850329 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature