Calibration and Localization of Optically Pumped Magnetometers Using Electromagnetic Coils.

In this paper, we propose a method to estimate the position, orientation, and gain of a magnetic field sensor using a set of (large) electromagnetic coils. We apply the method for calibrating an array of optically pumped magnetometers (OPMs) for magnetoencephalography (MEG). We first measure the magnetic fields of the coils at multiple known positions using a well-calibrated triaxial magnetometer, and model these discreetly sampled fields using vector spherical harmonics (VSH) functions.

Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design.

In this paper, we analyze spatial sampling of electro- (EEG) and magnetoencephalography (MEG), where the electric or magnetic field is typically sampled on a curved surface such as the scalp. By simulating fields originating from a representative adult-male head, we study the spatial-frequency content in EEG as well as in on- and off-scalp MEG. This analysis suggests that on-scalp MEG, off-scalp MEG and EEG can benefit from up to 280, 90 and 110 spatial samples, respectively.

Potential of on-scalp MEG: Robust detection of human visual gamma-band responses.

Electrophysiological signals recorded intracranially show rich frequency content spanning from near-DC to hundreds of hertz. Noninvasive electromagnetic signals measured with electroencephalography (EEG) or magnetoencephalography (MEG) typically contain less signal power in high frequencies than invasive recordings. Particularly, noninvasive detection of gamma-band activity (>30 Hz) is challenging since coherently active source areas are small at such frequencies and the available imaging methods have limited spatial resolution.

Adaptive neural network classifier for decoding MEG signals.

We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowing explorative analysis of neural sources informing classification. The proposed networks outperform traditional classifiers as well as more complex neural networks when decoding evoked and induced responses to different stimuli across subjects.

Optical Co-registration of MRI and On-scalp MEG.

To estimate the neural generators of magnetoencephalographic (MEG) signals, MEG data have to be co-registered with an anatomical image, typically an MR image. Optically-pumped magnetometers (OPMs) enable the construction of on-scalp MEG systems providing higher sensitivity and spatial resolution than conventional SQUID-based MEG systems. We present a co-registration method that can be applied to on-scalp MEG systems, regardless of the number of sensors.

On-scalp MEG system utilizing an actively shielded array of optically-pumped magnetometers.

The spatial resolution of magnetoencephalography (MEG) can be increased from that of conventional SQUID-based systems by employing on-scalp sensor arrays of e.g. optically-pumped magnetometers (OPMs).

Requirements for Coregistration Accuracy in On-Scalp MEG.

Recent advances in magnetic sensing has made on-scalp magnetoencephalography (MEG) possible. In particular, optically-pumped magnetometers (OPMs) have reached sensitivity levels that enable their use in MEG. In contrast to the SQUID sensors used in current MEG systems, OPMs do not require cryogenic cooling and can thus be placed within millimetres from the head, enabling the construction of sensor arrays that conform to the shape of an individual's head.

Neural Substrate for Metacognitive Accuracy of Tactile Working Memory.

The human prefrontal cortex (PFC) has been shown to be important for metacognition, the capacity to monitor and control one's own cognitive processes. Here we dissected the neural architecture of somatosensory metacognition using navigated single-pulse transcranial magnetic stimulation (TMS) to modulate tactile working memory (WM) processing. We asked subjects to perform tactile WM tasks and to give a confidence rating for their performance after each trial.