A hybrid steady-state visual evoked response-based brain-computer interface with MEG and EEG

• Published in: Expert systems with applications Vol. 223; p. 119736
• Main Authors: Li, Xiang, Chen, Jingjing, Shi, Nanlin, Yang, Chen, Gao, Puze, Chen, Xiaogang, Wang, Yijun, Gao, Shangkai, Gao, Xiaorong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2023
• Subjects: Electroencephalogram (EEG); Hybrid brain-computer interface; Magnetoencephalogram (MEG); Steady-state visual evoked response (SSVER); Steady-state visual evoked response (SSVER); Electroencephalogram (EEG); Hybrid brain-computer interface; Magnetoencephalogram (MEG)
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.119736

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.