A user-friendly visual brain-computer interface based on high-frequency steady-state visual evoked fields recorded by OPM-MEG

Abstract

Objective. Magnetoencephalography (MEG) shares a comparable time resolution with electroencephalography. However, MEG excels in spatial resolution, enabling it to capture even the subtlest and weakest brain signals for brain-computer interfaces (BCIs). Leveraging MEG’s capabilities, specifically with optically pumped magnetometers (OPM-MEG), proves to be a promising avenue for advancing MEG-BCIs, owing to its exceptional sensitivity and portability. This study harnesses the power of high-frequency steady-state visual evoked fields (SSVEFs) to build an MEG-BCI system that is flickering-imperceptible, user-friendly, and highly accurate. Approach. We have constructed a nine-command BCI that operates on high-frequency SSVEF (58–62 Hz with a 0.5 Hz interval) stimulation. We achieved this by placing the light source inside and outside the magnetic shielding room, ensuring compliance with non-magnetic and visual stimulus presentation requirements. Five participants took part in offline experiments, during which we collected six-channel multi-dimensional MEG signals along both the vertical (Z-axis) and tangential (Y-axis) components. Our approach leveraged the ensemble task-related component analysis algorithm for SSVEF identification and system performance evaluation. Main Results. The offline average accuracy of our proposed system reached an impressive 92.98% when considering multi-dimensional conjoint analysis using data from both the Z and Y axes. Our method achieved a theoretical average information transfer rate (ITR) of 58.36 bits min−1 with a data length of 0.7 s, and the highest individual ITR reached an impressive 63.75 bits min−1. Significance. This study marks the first exploration of high-frequency SSVEF-BCI based on OPM-MEG. These results underscore the potential and feasibility of MEG in detecting subtle brain signals, offering both theoretical insights and practical value in advancing the development and application of MEG in BCI systems.