Practical real-time MEG-based neural interfacing with optically pumped magnetometers

• Published in: BMC biology Vol. 19; no. 1; p. 158
• Main Authors: Wittevrongel, Benjamin, Holmes, Niall, Boto, Elena, Hill, Ryan, Rea, Molly, Libert, Arno, Khachatryan, Elvira, Van Hulle, Marc M, Bowtell, Richard, Brookes, Matthew J
• Format: Journal Article
• Language: English
• Published: England BioMed Central 10.08.2021; BMC
• Subjects: Accuracy; Brain; Brain-computer interface (BCI); Communication; Computer applications; Confidence intervals; Electroencephalography; Event-related field (ERF); Event-related potential (ERP); Experiments; Human-computer interface; Humans; Implants; Interfaces; Magnetoencephalography; Magnetoencephalography (MEG); Magnetometers; Methodology; Optically pumped magnetometers (OPM); Real time; Signal quality; Steady-state visual evoked potential (SSVEP); Topography; Magnetoencephalography (MEG); Optically pumped magnetometers (OPM); Steady-state visual evoked potential (SSVEP); Electroencephalography (EEG); Event-related field (ERF); Event-related potential (ERP); Brain-computer interface (BCI)
• ISSN: 1741-7007; 1741-7007
• DOI: 10.1186/s12915-021-01073-6

Brain-computer interfaces decode intentions directly from the human brain with the aim to restore lost functionality, control external devices or augment daily experiences. To combine optimal performance with wide applicability, high-quality brain signals should be captured non-invasively. Magnetoencephalography (MEG) is a potent candidate but currently requires costly and confining recording hardware. The recently developed optically pumped magnetometers (OPMs) promise to overcome this limitation, but are currently untested in the context of neural interfacing. In this work, we show that OPM-MEG allows robust single-trial analysis which we exploited in a real-time 'mind-spelling' application yielding an average accuracy of 97.7%. This shows that OPM-MEG can be used to exploit neuro-magnetic brain responses in a practical and flexible manner, and opens up new avenues for a wide range of new neural interface applications in the future.