Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces

TL;DR

This paper introduces a least-squares transformation method for SSVEP-based brain-computer interfaces that significantly reduces calibration data needs, enhancing practicality for real-world applications.

Contribution

It proposes a novel cross-subject transfer learning approach using LST to minimize training data, improving SSVEP BCI efficiency and usability.

Findings

LST reduces training templates needed for 40-class SSVEP BCI.

LST enables near-zero-training, plug-and-play high-speed BCIs.

Improves practicality of SSVEP BCIs in real-world scenarios.

Abstract

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown its robustness in facilitating high-efficiency communication. State-of-the-art training-based SSVEP decoding methods such as extended Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA) are the major players that elevate the efficiency of the SSVEP-based BCIs through a calibration process. However, due to notable human variability across individuals and within individuals over time, calibration (training) data collection is non-negligible and often laborious and time-consuming, deteriorating the practicality of SSVEP BCIs in a real-world context. This study aims to develop a cross-subject transferring approach to reduce the need for collecting training data from a test user with a newly proposed least-squares transformation (LST) method. Study results show the capability of the LST in reducing the number of training templates required for a 40-class SSVEP BCI. The LST method may lead to numerous real-world applications using near-zero-training/plug-and-play high-speed SSVEP BCIs.