A hybrid steady-state visual evoked response-based brain-computer interface with MEG and EEG
Xiang Li, Jingjing Chen, Nanlin Shi, Chen Yang, Puze Gao, Xiaogang Chen, Yijun Wang, Shangkai Gao, Xiaorong Gao
Abstract
While recent developments in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have enabled a bridge between the brain and external devices with relatively high communication speed, there is still room for improvement. Notably, the phenomenon of “BCI illiteracy,” which refers to the 15%–30% of people who struggle to type or control devices using BCI, remains unsolved, limiting the practical application of BCI systems. The EEG-based BCIs performance is constrained by the low-quality scalp EEG signals due to the attenuation and distortion of the skull. To address these limitations, this study proposes a hybrid BCI system combining EEG with magnetoencephalogram (MEG), a neuroimaging technology not influenced by the volume conduction effect, to boost BCI performance by enhancing signal quality. Comparative experiments involving 22 subjects showed that the steady-state visual evoked response (SSVER) from MEG has a wider range of effective bandwidth and higher signal-to-noise ratio than EEG. Moreover, differences in the spectral and spatiotemporal characteristics of MEG and EEG explain better performance. Simultaneous MEG-EEG recording experiments suggested that the hybrid MEG-EEG BCI achieved a significantly higher information transfer rate than either modality alone (hybrid: 312 ± 17 bits/min, MEG: 272 ± 17 bits/min, EEG: 240 ± 27 bits/min). Moreover, the 40-target classification accuracy of “BCI illiterate” increased from 50% to 95% with the help of MEG. These results highlight the methodological advantages of a hybrid MEG-EEG BCI, suggesting a promising paradigm for implementing high-speed BCIs.