Magnetoencephalography (MEG) is an important part of epilepsy evaluations because of its unsurpassed ability to detect interictal epileptiform discharges (IEDs). This ability may be improved by next-generation MEG sensors, where sensors are placed directly on the scalp instead of in a fixed-size helmet, as in today’s conventional MEG systems. In order to investigate the usefulness of on-scalp MEG measurements we performed the first-ever measurements of on-scalp MEG on an epilepsy patient. The measurement was conducted as a benchmarking study, with special focus on IED detection. An on-scalp high-temperature SQUID system was utilized alongside a conventional low-temperature “in-helmet” SQUID system. EEG was co-registered during both recordings. Visual inspection of IEDs in the raw on-scalp MEG data was unfeasible why a novel machine learning-based IED-detection algorithm was developed to guide IED detection in the on-scalp MEG data. A total of 24 IEDs were identified visually from the conventional in-helmet MEG session (of these, 16 were also seen in the EEG data; eight were detected only by MEG). The on-scalp MEG data contained a total of 47 probable IEDs of which 16 IEDs were co-registered by the EEG, and 31 IEDs were on-scalp MEG-unique IEDs found by the IED detection algorithm. We present a successful benchmarking study where on-scalp MEG are compared to conventional in-helmet MEG in a temporal lobe epilepsy patient. Our results demonstrate that on-scalp MEG measurements are feasible on epilepsy patients, and indicate that on-scalp MEG might capture IEDs not seen by other non-invasive modalities.

Detection of interictal epileptiform discharges: A comparison of on-scalp MEG and conventional MEG measurements