Decoding with multivariate pattern analysis is superior for optically pumped magnetometer-based magnetoencephalography compared to superconducting quantum interference device-based systems
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Background
Multivariate pattern analysis (MVPA) has become an increasingly important method for decoding distributed brain activity from neural electrophysiological recordings by leveraging both temporal and spatial features. These multivariate approaches have proven important for both cognitive neuroscience and brain-computer interfaces. MVPA might benefit from magnetoencephalography (MEG) systems based on optically pumped magnetometers (OPMs), as these sensors can be placed closer to the scalp, providing higher spatial resolution compared to conventional MEG systems that rely on superconducting quantum interference devices (SQUIDs). As OPM-based MEG systems become available at more institutions, it is essential to experimentally compare their performance with traditional SQUID-based systems using MVPA.
Methods
We adapted a visual object-word paradigm from a previous study, originally implemented on a TRIUX MEGIN SQUID system, to the FieldLine HEDscan OPM system. Participants were recruited and we recorded their ingoing brain activity while did the same task in both systems. Visual stimuli of different objects were presented alternately in two modalities: pictures and the corresponding written words. For each modality, MVPA was used to classify the objects from OPM and SQUID magnetometers data respectively. To further investigate the advantages of OPM, we evaluated the effect classification accuracy of two spatial factors by controlling the number of sensors included and the spatial frequency content of the sensor data.
Results
We found higher time-resolved decoding accuracy for the OPM compared to the SQUID data. Moreover, OPMs show higher classification performance compared to SQUIDs when controlling for the same number of sensors; consistently, the OPM system required fewer sensors to reach the performance limit of the SQUID system. Our analysis considering the spatial frequency content of the signal revealed that decoding accuracy plateaued for the SQUID system at lower spatial frequencies while the performance of the OPM system continued to improve when higher-order spatial components were included.
Conclusion
Our OPM-MEG system outperformed the SQUID-MEG system on MVPA on decoding of visual processing. This advantage of OPM is driven by its higher spatial resolution, resulting from the sensors being positioned closer to the head and thus able to capture higher spatial frequency components of the brain signal. OPM may facilitate cognitive neuroscience research as well as brain-computer interfaces by providing higher sensitivity when employing paradigms using multi-variate data analysis.
