Extended Signal-Space Separation Method for Improved Interference Suppression in MEG.

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.

Virtual MEG Helmet: Computer Simulation of an Approach to Neuromagnetic Field Sampling.

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.

Improving MEG performance with additional tangential sensors.

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.

Suppressing multi-channel ultra-low-field MRI measurement noise using data consistency and image sparsity.

Ultra-low-field (ULF) MRI (B 0 = 10-100 µT) 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.

Hybrid ultra-low-field MRI and magnetoencephalography system based on a commercial whole-head neuromagnetometer.

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.

Short-term memory functions of the human fetus recorded with magnetoencephalography.

Studies in fetuses and in prematurely born infants show that auditory discriminative skills are present prior to birth. The magnetic fields generated by the fetal brain activity pass the maternal tissues and, despite their weakness, can be detected externally using MEG. Recent studies on the auditory evoked magnetic responses show that the fetal brain responds to sound onset. In contrast, higher-level auditory skills, such as those involving discriminative and memory functions, were not so far studied in fetuses with MEG. Here we show that fetal responses related to discriminating sounds can be recorded, implicating that the auditory change-detection system is functional. These results open new views to developmental neuroscience by enabling one to determine the sensory capabilities as well as the extent and accuracy of the short-term memory system of the fetus, and, further, to follow the development of these crucial processes.

Suppression of interference and artifacts by the Signal Space Separation Method.

Multichannel measurement with hundreds of channels oversamples a curl-free vector field, like the magnetic field in a volume free of sources. This is based on the constraint caused by the Laplace's equation for the magnetic scalar potential; outside of the source volume the signals are spatially band limited. A functional solution of Laplace's equation enables one to separate the signals arising from the sphere enclosing the interesting sources, e.g. the currents in the brain, from the magnetic interference. Signal space separation (SSS) is accomplished by calculating individual basis vectors for each term of the functional expansion to create a signal basis covering all measurable signal vectors. Because the SSS basis is linearly independent for all practical sensor arrangements, any signal vector has a unique SSS decomposition with separate coefficients for the interesting signals and signals coming from outside the interesting volume. Thus, SSS basis provides an elegant method to remove external disturbances. The device-independent SSS coefficients can be used in transforming the interesting signals to virtual sensor configurations. This can also be used in compensating for distortions caused by movement of the object by modeling it as movement of the sensor array around a static object. The device-independence of the decomposition also enables physiological DC phenomena to be recorded using voluntary head movements. When used with properly designed sensor array, SSS does not affect the morphology or the signal-to-noise ratio of the interesting signals.

Femtotesla magnetic field measurement with magnetoresistive sensors.

The measurement of magnetic fields in the femtotesla (fT, 10(-15) tesla) range is important for applications such as magnetometry, quantum computing, solid-state nuclear magnetic resonance, and magnetoencephalography. The only sensors capable of detecting these very small fields have been based on low-temperature superconducting quantum interference devices operating at 4.2 kelvin. We present a magnetic field sensor that combines a superconducting flux-to-field transformer with a low-noise giant magnetoresistive sensor. The sensor can be operated up to 77 kelvin. Our small-size prototype provides the capability of measuring 32 fT.