Front-end Replication Dynamic Window (FRDW) for Online Motor Imagery Classification

TL;DR

The paper introduces the FRDW algorithm that enhances online motor imagery classification by combining dynamic windowing with front-end replication, leading to faster and more accurate EEG-based BCI decoding.

Contribution

It presents a novel FRDW method that improves classification speed and accuracy in EEG-based BCIs, validated through extensive experiments and competition success.

Findings

FRDW significantly increases information transfer rate in MI decoding.

FRDW improves classification accuracy with shorter test trials.

FRDW can be used for training data augmentation.

Abstract

Motor imagery (MI) is a classical paradigm in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Online accurate and fast decoding is very important to its successful applications. This paper proposes a simple yet effective front-end replication dynamic window (FRDW) algorithm for this purpose. Dynamic windows enable the classification based on a test EEG trial shorter than those used in training, improving the decision speed; front-end replication fills a short test EEG trial to the length used in training, improving the classification accuracy. Within-subject and cross-subject online MI classification experiments on three public datasets, with three different classifiers and three different data augmentation approaches, demonstrated that FRDW can significantly increase the information transfer rate in MI decoding. Additionally, FR can also be used in training data augmentation. FRDW helped win national champion of the China BCI Competition in 2022.