The first half of the project has focused on the development of the measurement system. To this end, we have constructed a helmet that holds optically-pumped magnetometers at freely-selectable locations around the head. In this helmet, the sensors can be adjusted to just touch the scalp of the subject. To ensure proper operation of the sensors and to reduce external magnetic interference, we have also designed and constructed a compensation coil system around the sensor array; the currents in the coils are dynamically controlled by signals from the magnetometers such that the magnetic field is kept below 1 nT (nanotesla) (Iivanainen et al., 2019a). We have also developed a system that optically measures the positions and orientations of the sensors with respect to the head surface of the subject. By combining this information with the structural magnetic resonance images of the subject, we can accurately estimate the locations of the neural sources underlying the measured magnetic signals (Zetter et al. 2018; 2019).
We performed the first human measurements by presenting the subjects with dynamic visual stimuli. The HRMEG system detected clear visually-induced responses, including suppression of the alpha activity at around 10 Hz and elevation of both narrow-band (30-40 Hz) and broad-band gamma (above 50 Hz) activity. We compared the HRMEG results with those obtained with conventional MEG in the same subjects and found that HRMEG yielded better signal-to-noise ratio and was able to better separate activatios in the two gamma bands (Iivanainen et al., 2019b).
In anticipation of applying the HRMEG system to brain–computer interfacing, we have developed a convolutional neural network classifier that allows training across multiple subjects' MEG data and rapid adaptation to the invidual's MEG responses (Zubarev et al., 2019).
The main result of the first half of the project is a working HRMEG system, with which we have successfully performed initial human brain measurements.
The second half of the project was severely affected by the COVID restrictions as laboratory work and human measurements were restricted to various degrees for about a year. Yet, we were able to continue improving the measurement set-up, both in terms of hardware and software-based methods. We developed a system for better control of the ambient magnetic field (Mäkinen et al., 2020; Zetter et al., 2020; Iivanainen et al., 2021), which enabled us to prepare for recordings in epilepsy patients. We have also developed software-based interference suppression methods (Helle et al., 2021) and means for localizing and calibrating the sensors (Iivanainen et al., 2022).
We extended human measurements to more natural stimuli. First, we studied how well we can pick up the induced gamma-band responses to still pictures; all image types induced detectable gamma-band activity whose level depended on the image type (Grön, 2022). We then used a movie as a dynamic, naturalistic stimulus. We showed a 12-min clip of the Hollywood movie "Forrest Gump" to 10 participants while their brain activity was measured by the HRMEG system. We observed that brain signals significantly correlated across the viewers (Forsman, 2021).
We also tested recording animal brain activity with the HRMEG approach. Our pilot experiment in a domestic cat demonstrated that feline brain activity could be measured with high fidelity with this non-invasive technique.
In summary, the HRMEG project has yielded a versatile, high-resolution MEG system and that system has been successfully applied to study human and animal brain activity.
