Design and Quantitative Evaluation of the Fitting Range of a Rigid Helmet for Optically Pumped Magnetometer Magnetoencephalography

YAN Bing; JI Jie; PENG Yuming; NIU Yanxiong; SUN Chang; DING Ming

doi:10.11999/JEIT260860

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YAN Bing, JI Jie, PENG Yuming, NIU Yanxiong, SUN Chang, DING Ming. Design and Quantitative Evaluation of the Fitting Range of a Rigid Helmet for Optically Pumped Magnetometer Magnetoencephalography[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260860

Citation:

YAN Bing, JI Jie, PENG Yuming, NIU Yanxiong, SUN Chang, DING Ming. Design and Quantitative Evaluation of the Fitting Range of a Rigid Helmet for Optically Pumped Magnetometer Magnetoencephalography[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260860

Citation:

YAN Bing, JI Jie, PENG Yuming, NIU Yanxiong, SUN Chang, DING Ming. Design and Quantitative Evaluation of the Fitting Range of a Rigid Helmet for Optically Pumped Magnetometer Magnetoencephalography[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260860

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Design and Quantitative Evaluation of the Fitting Range of a Rigid Helmet for Optically Pumped Magnetometer Magnetoencephalography

doi: 10.11999/JEIT260860 cstr: 32379.14.JEIT260860

School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, 100191, China

Received Date: 2026-06-16
Accepted Date: 2026-10-15
Rev Recd Date: 2026-10-14

Available Online: 2026-10-22

Abstract

Abstract

Objective Magnetoencephalography (MEG) is a non-invasive technique for detecting weak magnetic fields generated by neuronal activity. Compared with conventional superconducting quantum interference device-based MEG systems, optically pumped magnetometer-based magnetoencephalography (OPM-MEG) allows sensors to operate at room temperature and to be placed closer to the scalp, thereby improving signal detection efficiency and supporting wearable system design. In practical OPM-MEG systems, rigid helmets can provide stable sensor positions and improve repeated measurement consistency. However, because head size varies across subjects, a fixed-size rigid helmet may increase the sensor-to-scalp distance for smaller heads, resulting in reduced forward-field strength and limited detectable coverage. Existing OPM-MEG helmet studies have mainly focused on system implementation and experimental validation, whereas quantitative methods for evaluating the size-adaptation range of fixed-size rigid helmets remain insufficient. This study proposes a standard-head-model-based design and multidimensional quantitative evaluation method for an OPM-MEG rigid helmet, aiming to provide guidance for helmet standardization, size planning, and engineering application. Methods The ICBM152 standard head model was used as the geometric reference for helmet design. The scalp surface was extracted and truncated according to the brain coverage requirement, and a smooth inner-wall surface was generated. Following the principles of maintaining basic wearing clearance, reducing the sensor-to-cortex distance, and ensuring whole-brain coverage, a 64-channel OPM-MEG sensor array was arranged to cover the frontal, parietal, temporal, and occipital regions. Based on the inner-wall surface and sensor positions, a rigid helmet structure with sensor slots and ear openings was constructed, considering structural stability, 3D-printing feasibility, and wearing comfort. To evaluate the size-adaptation range, the standard head model was defined as the 1.0-scale model, and six head-size models with scale factors of 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 were generated. During scaling, the helmet and sensor array remained fixed, while the head model was proportionally resized with the top of the head kept unchanged. A 306-channel SQUID-MEG system was used as the reference baseline. The Frobenius norm of the lead field was used to evaluate forward-field detection capability, while undetectable voxels were used to quantify effective detectable coverage. Mean ratio, median ratio, spatial coverage rate, undetectable-voxel ratio, Jaccard similarity, newly undetectable voxels, and recovered detectable voxels were calculated for comparison. Results and Discussions The simulation results showed that head-size mismatch had a clear influence on the detection capability of the fixed-size OPM-MEG rigid helmet. As the head scale increased from 0.5 to 1.0, the Frobenius norm over the cortical surface increased continuously, indicating enhanced forward-field strength when cortical sources became closer to the fixed sensor array. The mean Frobenius norm increased from 1.81×10−9 at the 0.5-scale model to 2.82×10−9 at the 1.0-scale model, corresponding to 88.9% and 139.0% of the SQUID-MEG reference baseline, respectively. The median ratio showed a similar increase from 87.1% to 139.1%. Based on linear interpolation, the mean and median Frobenius norm ratios reached the SQUID-MEG reference level at head-scale factors of approximately 0.66 and 0.69, respectively. However, when spatial coverage was considered, a stricter threshold was obtained: a head-scale factor of approximately 0.89 was required for 95% of cortical source points to reach at least 95% of the reference detection capability. This indicates that global statistical indicators may reflect the average detection capability, whereas spatial coverage can more sensitively reveal local performance degradation. The undetectable-voxel analysis provided complementary evidence for detectable coverage. As the head scale decreased from 1.0 to 0.5, undetectable regions gradually expanded, mainly in lower and peripheral areas of the region of interest. The number of undetectable voxels increased from 1809 at the 1.0-scale model to 4396 at the 0.5-scale model, corresponding to 11.56% and 28.10% of all voxels, respectively. In contrast, the SQUID-MEG reference system had 5063 undetectable voxels, accounting for 32.36%. Therefore, within the tested range of 0.5–1.0, the designed OPM-MEG rigid helmet maintained fewer undetectable voxels than the SQUID-MEG baseline. Nevertheless, newly undetectable voxels appeared when the head scale decreased to 0.6 and 0.5, indicating local degradation of detectable coverage. These results suggest that the size-adaptation range of a fixed-size rigid helmet is not a single fixed value, but depends on the selected evaluation criterion and application scenario. Conclusions This study proposes a standard-head-model-based design and quantitative evaluation method for the size-adaptation range of an OPM-MEG rigid helmet. A 64-channel rigid helmet was designed using the ICBM152 standard head model, and its performance was evaluated under different head-size conditions by combining forward-field strength and detectable-coverage metrics. When the mean and median Frobenius norm ratios were used as overall performance criteria, the lower adaptation limits were approximately 0.66 and 0.69 times the standard head model. When spatial coverage was used as a stricter criterion, the lower limit increased to approximately 0.89. Based on the undetectable-voxel analysis, the helmet maintained better overall detectable coverage than the SQUID-MEG reference within the tested range, while the preferred engineering adaptation limit was approximately 0.7. These findings provide quantitative guidance for OPM-MEG rigid helmet standardization, size planning, applicable population analysis, and engineering application.
- OPM-MEG,
- Rigid helmet design,
- size-adaptation range,
- Frobenius norm,
- Undetectable voxel

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References(26)

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