Objective: Magnetoencephalography (MEG) signals typically reflect a mixture of neuromagnetic fields, subject-related artifacts, external interference and sensor noise. Even inside a magnetically shielded room, external interference can be significantly stronger than brain signals. Methods such as signal-space projection (SSP) and signal-space separation (SSS) have been developed to suppress this residual interference, but their performance might not be sufficient in cases of strong interference or when the sources of interference change over time. Methods: Here we suggest a new method, extended signal-space separation (eSSS), which combines a physical model of the magnetic fields (as in SSS) with a statistical description of the interference (as in SSP). We demonstrate the performance of this method via simulations and experimental MEG data. Results: The eSSS method clearly outperforms SSS and SSP in interference suppression regardless of the extent of a priori information available on the interference sources. We also show that the method does not cause location or amplitude bias in dipole modeling. Conclusion: Our eSSS method provides better data quality than SSP or SSS and can be readily combined with other SSS-based methods, such as spatiotemporal SSS or head movement compensation. Thus, eSSS extends and complements the interference suppression techniques currently available for MEG. Significance: Due to its ability to suppress external interference to the level of sensor noise, eSSS can facilitate single-trial data analysis, exemplified in automated analysis of epileptic data. Such an enhanced suppression is especially important in environments with large interference fields.

Head movements during an MEG recording are commonly considered an obstacle. In this computer simulation study, we introduce an approach, the virtual MEG helmet (VMH), which employs the head movements for data quality improvement. With a VMH, a denser MEG helmet is constructed by adding new sensors corresponding to different head positions. Based on the Shannon's theory of communication, we calculated the total information as a figure of merit for comparing the actual 306-sensor Elekta Neuromag helmet to several types of the VMH. As source models, we used simulated randomly distributed source current (RDSC), simulated auditory and somatosensory evoked fields. Using the RDSC model with the simulation of 360 recorded events, the total information (bits/sample) was 989 for the most informative single head position and up to 1272 for the VMH (addition of 28.6%). Using simulated AEFs, the additional contribution of a VMH was 12.6% and using simulated SEF only 1.1%. For the distributed and bilateral sources, a VMH can provide a more informative sampling of the neuromagnetic field during the same recording time than measuring the MEG from one head position. VMH can, in some situations, improve source localization of the neuromagnetic fields related to the normal and pathological brain activity. This should be investigated further employing real MEG recordings.

Recently, the signal space separation (SSS) method, based on the multipole expansion of the magnetic field, has become increasingly important in magnetoencephalography (MEG). Theoretical arguments and simulations suggest that increasing the asymmetry of the MEG sensor array from the traditional, rather symmetric geometry can significantly improve the performance of the method. To test this concept, we first simulated addition of tangentially oriented standard sensor elements to the existing 306-channel Elekta Neuromag sensor array, and evaluated and optimized the performance of the new sensor configuration. Based on the simulation results, we then constructed a prototype device with 18 additional tangential triple-sensor elements and a total of 360 channels. The experimental results from the prototype are largely in agreement with the simulations. In application of the spatial SSS method, the 360-channel device shows an approximately 100% increase in software shielding capability, while residual reconstruction noise of evoked responses is decreased by 20%. Further, the new device eliminates the need for regularization while applying the SSS method. In conclusion, we have demonstrated in practice the benefit of reducing the symmetry of the MEG array, without the need for a complete redesign.

Ultra-low-field (ULF) MRI (B-0 = 10-100 mu T) typically suffers from a low signal-to-noise ratio (SNR). While SNR can be improved by pre-polarization and signal detection using highly sensitive superconducting quantum interference device (SQUID) sensors, we propose to use the inter-dependency of the k-space data from highly parallel detection with up to tens of sensors readily available in the ULF MRI in order to suppress the noise. Furthermore, the prior information that an image can be sparsely represented can be integrated with this data consistency constraint to further improve the SNR. Simulations and experimental data using 47 SQUID sensors demonstrate the effectiveness of this data consistency constraint and sparsity prior in ULF-MRI reconstruction.

Ultra‐low‐field MRI uses microtesla fields for signal encoding and sensitive superconducting quantum interference devices for signal detection. Similarly, modern magnetoencephalography (MEG) systems use arrays comprising hundreds of superconducting quantum interference device channels to measure the magnetic field generated by neuronal activity. In this article, hybrid MEG‐MRI instrumentation based on a commercial whole‐head MEG device is described. The combination of ultra‐low‐field MRI and MEG in a single device is expected to significantly reduce coregistration errors between the two modalities, to simplify MEG analysis, and to improve MEG localization accuracy. The sensor solutions, MRI coils (including a superconducting polarizing coil), an optimized pulse sequence, and a reconstruction method suitable for hybrid MEG‐MRI measurements are described. The performance of the device is demonstrated by presenting ultra‐low‐field‐MR images and MEG recordings that are compared with data obtained with a 3T scanner and a commercial MEG device. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

R.J. Ilmoniemi, J. Dabek, F.-H. Lin, J.O. Nieminen, L.T. Parkkonen, P.T. Vesanen, K.C.J. Zevenhoven, A.V. Zhdanov, J. Luomahaara, J. Hassel, J. Penttila, J. Simola, A.I. Ahonen, and J.P. Makela Dept. Biomedical Engineering and Computational Science, Aalto University School of Science, Espoo, Finland; Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan; Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States; Elekta Oy, Helsinki, Finland; BioMag Laboratory, HUSLAB, Helsinki University Central Hospital, Helsinki, Finland; VTT Technical Research Centre of Finland, Espoo, Finland; Aivon Oy, Espoo, Finland

In the context of biomagnetism, a magnetically shielded room (MSR) is designed for shielding against external magnetic fields. Recently, several applications, such as combined structural magnetic resonance imaging (MRI) and functional magnetoencephalography (MEG), have emerged that require applying relatively strong magnetic fields inside the MSR. These magnetic fields induce eddy currents and magnetize the MSR walls that are made of materials with high permeability and conductivity. These eddy currents and magnetization generate secondary magnetic fields inside the room that disturb, e.g., combined MEG-MRI by affecting sample spins and by exceeding the available dynamic range of the magnetic field sensors. In this work, static and dynamic magnetic fields applied inside an MSR are studied both theoretically and experimentally. Using a spherical model, analytical expressions are derived for the amplitudes and time constants of the various secondary magnetic field modes. These predictions are validated by comparison with experimental measurements in a rectangular MSR. The results of this study facilitate, e.g., the design of coils compatible with an MSR; a self-shielded coil is presented that decreases the secondary magnetic fields to a small fraction.

Flux trapping and random flux movement are common problems in superconducting thin-film devices. Ultrasensitive magnetic field sensors based on superconducting quantum interference devices (SQUIDs) coupled to large pickup coils are especially vulnerable to strong external fields. The issue has become particularly relevant with the introduction of SQUID-based ultra-low-field (ULF) nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) techniques. In this paper, we study the constraints of thin-film-based magnetometers and gradiometers as exposed to magnetic field sequences of ULF MRI. In particular, we address issues such as response recovery, transient noise, magnetization and behaviour under shielded room conditions after prepolarization. As a result, we demonstrate sensors that are suitable for a combined multi-channel magnetoencephalography (MEG) and MRI imaging system.