An F-ratio-Based Method for Estimating the Number of Active Sources in MEG

Magnetoencephalography (MEG) is a powerful technique for studying the human brain function. However, accurately estimating the number of sources that contribute to the MEG recordings remains a challenging problem due to the low signal-to-noise ratio (SNR), the presence of correlated sources, inaccuracies in head modeling, and variations in individual anatomy. To address these issues, our study introduces a robust method for accurately estimating the number of active sources in the brain based on the F-ratio statistical approach, which allows for a comparison between a full model with a higher number of sources and a reduced model with fewer sources. Using this approach, we developed a formal statistical procedure that sequentially increases the number of sources in the multiple dipole localization problem until all sources are found. Our results revealed that the selection of thresholds plays a critical role in determining the method`s overall performance, and appropriate thresholds needed to be adjusted for the number of sources and SNR levels, while they remained largely invariant to different inter-source correlations, modeling inaccuracies, and different cortical anatomies. By identifying optimal thresholds and validating our F-ratio-based method in simulated, real phantom, and human MEG data, we demonstrated the superiority of our F-ratio-based method over existing state-of-the-art statistical approaches, such as the Akaike Information Criterion (AIC) and Minimum Description Length (MDL). Overall, when tuned for optimal selection of thresholds, our method offers researchers a precise tool to estimate the true number of active brain sources and accurately model brain function

Advancing Brain-Computer Interface System Performance in Hand Trajectory Estimation with NeuroKinect

Brain-computer interface (BCI) technology enables direct communication between the brain and external devices, allowing individuals to control their environment using brain signals. However, existing BCI approaches face three critical challenges that hinder their practicality and effectiveness: a) time-consuming preprocessing algorithms, b) inappropriate loss function utilization, and c) less intuitive hyperparameter settings. To address these limitations, we present \textit{NeuroKinect}, an innovative deep-learning model for accurate reconstruction of hand kinematics using electroencephalography (EEG) signals. \textit{NeuroKinect} model is trained on the Grasp and Lift (GAL) tasks data with minimal preprocessing pipelines, subsequently improving the computational efficiency. A notable improvement introduced by \textit{NeuroKinect} is the utilization of a novel loss function, denoted as $\mathcal{L}_{\text{Stat}}$. This loss function addresses the discrepancy between correlation and mean square error in hand kinematics prediction. Furthermore, our study emphasizes the scientific intuition behind parameter selection to enhance accuracy. We analyze the spatial and temporal dynamics of the motor movement task by employing event-related potential and brain source localization (BSL) results. This approach provides valuable insights into the optimal parameter selection, improving the overall performance and accuracy of the \textit{NeuroKinect} model. Our model demonstrates strong correlations between predicted and actual hand movements, with mean Pearson correlation coefficients of 0.92 ($\pm$0.015), 0.93 ($\pm$0.019), and 0.83 ($\pm$0.018) for the X, Y, and Z dimensions. The precision of \textit{NeuroKinect} is evidenced by low mean squared errors (MSE) of 0.016 ($\pm$0.001), 0.015 ($\pm$0.002), and 0.017 ($\pm$0.005) for the X, Y, and Z dimensions, respectively

An F-ratio-based method for estimating the number of active sources in MEG

IntroductionMagnetoencephalography (MEG) is a powerful technique for studying the human brain function. However, accurately estimating the number of sources that contribute to the MEG recordings remains a challenging problem due to the low signal-to-noise ratio (SNR), the presence of correlated sources, inaccuracies in head modeling, and variations in individual anatomy.MethodsTo address these issues, our study introduces a robust method for accurately estimating the number of active sources in the brain based on the F-ratio statistical approach, which allows for a comparison between a full model with a higher number of sources and a reduced model with fewer sources. Using this approach, we developed a formal statistical procedure that sequentially increases the number of sources in the multiple dipole localization problem until all sources are found.ResultsOur results revealed that the selection of thresholds plays a critical role in determining the method's overall performance, and appropriate thresholds needed to be adjusted for the number of sources and SNR levels, while they remained largely invariant to different inter-source correlations, translational modeling inaccuracies, and different cortical anatomies. By identifying optimal thresholds and validating our F-ratio-based method in simulated, real phantom, and human MEG data, we demonstrated the superiority of our F-ratio-based method over existing state-of-the-art statistical approaches, such as the Akaike Information Criterion (AIC) and Minimum Description Length (MDL).DiscussionOverall, when tuned for optimal selection of thresholds, our method offers researchers a precise tool to estimate the true number of active brain sources and accurately model brain function

International Federation of Clinical Neurophysiology (IFCN) – EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies

In 1999, the International Federation of Clinical Neurophysiology (IFCN) published “IFCN Guidelines for topographic and frequency analysis of EEGs and EPs” (Nuwer et al., 1999). Here a Workgroup of IFCN experts presents unanimous recommendations on the following procedures relevant for the topographic and frequency analysis of resting state EEGs (rsEEGs) in clinical research defined as neurophysiological experimental studies carried out in neurological and psychiatric patients: (1) recording of rsEEGs (environmental conditions and instructions to participants; montage of the EEG electrodes; recording settings); (2) digital storage of rsEEG and control data; (3) computerized visualization of rsEEGs and control data (identification of artifacts and neuropathological rsEEG waveforms); (4) extraction of “synchronization” features based on frequency analysis (band-pass filtering and computation of rsEEG amplitude/power density spectrum); (5) extraction of “connectivity” features based on frequency analysis (linear and nonlinear measures); (6) extraction of “topographic” features (topographic mapping; cortical source mapping; estimation of scalp current density and dura surface potential; cortical connectivity mapping), and (7) statistical analysis and neurophysiological interpretation of those rsEEG features. As core outcomes, the IFCN Workgroup endorsed the use of the most promising “synchronization” and “connectivity” features for clinical research, carefully considering the limitations discussed in this paper. The Workgroup also encourages more experimental (i.e. simulation studies) and clinical research within international initiatives (i.e., shared software platforms and databases) facing the open controversies about electrode montages and linear vs. nonlinear and electrode vs. source levels of those analyses

Magnetoencephalographic investigations of human primary auditory cortex responses to multiple amplitude modulated tones

Der zeitlichen Verläufe von Sprach- oder Musikklängen enthalten Amplitudenuktuationen mit unterschiedlichen Rhythmen die Träger der Information sind. Unsere Studie liefert wertvolle neue Erkenntnisse darüber, wie und in welcher Hirnregion komplexe Töne verarbeitet werden und mit anderen Komponenten des zentralen auditorischen Kortex interagieren. Die 40 Hz SSR hat sich dabei als wertvoller Parameter bei der Erforschung von Sprachverarbeitung und Störungen des zentralen Kortex erwiesen. Ein reiner Ton, mit zwei Sinusschwingungen von 38 und 40 Hz moduliert, wird wahrgenommen als eine 2 Hz Schwankung der Lautheit und eine Rauhigkeit, die den Modulationsfrequenzen bei 40 Hz entspricht. Korrespondierende SSRs bei 2, 38 und 40 Hz wurden mit dem MEG erfasst, das die Lokalisation überlappender Quellen im primären auditorischen Kortex ermöglichte. Der allgemeinere Fall mehrfacher AM Töne mit verschiedenen Trägerfrequenzen zeigte, dass die Antwort auf einen AM Ton unterdrückt wird, wenn ein zweiter AM Stimulus gleichzeitig dargeboten wird. Die Ergebnisse lassen eine zentrale Generierung der 40 Hz SSR annehmen

Simultaneous EEG-fMRI : novel methods for EEG artefacts reduction at source

This thesis describes the development and application of novel techniques to reduce the EEG artefacts at source during the simultaneous acquisition of EEG and fMRI data. The work described in this thesis was carried out by the author in the Sir Peter Mansfield Magnetic Resonance Centre, School of Physics & Astronomy at the University of Nottingham, between October 2010 and January 2013. Large artefacts compromise EEG data quality during simultaneous fMRI. These artefact voltages pose heavy demands on the bandwidth and dynamic range of EEG amplifiers and mean that even small fractional variations in the artefact voltages give rise to significant residual artefacts after correction, which can easily swamp signals from brain activity. Therefore any intrinsic reduction in the magnitude of the artefacts would be highly advantageous, allowing data with a higher bandwidth to be acquired without amplifier saturation, and facilitating improved detection of brain activity. This thesis firstly explores a new method for reducing the gradient artefact (GA), which is induced in EEG data recorded during concurrent MRI, by investigating the effects of the cable configuration on the characteristics of the GA. This work showed that the GA amplitude and its sensitivity to movement of the cabling is reduced by minimising wire loop areas in the cabling between the EEG cap and amplifier. Another novel approach for reducing the magnitude and variability of the artefacts is the use of an EEG cap that incorporates electrodes embedded in a reference layer, which has a similar conductivity to tissue and is electrically isolated from the scalp. With this arrangement, the artefact voltages produced on the reference layer leads are theoretically similar to those induced in the scalp leads, but neuronal signals are not detected in the reference layer. Therefore taking the difference of the voltages in the reference and scalp channels should reduce the artefacts, without affecting sensitivity to neuronal signals. The theoretical efficacy of artefact correction that can be achieved by using this new reference layer artefact subtraction (RLAS) method was investigated. This was done through separate electromagnetic simulations of the artefacts induced in a hemispherical reference layer and a spherical volume conductor in a time-varying magnetic field and the results showed that similar artefacts are induced on the surface of both conductors. Simulations are also performed to find the optimal design for an RLAS system, by varying the geometry of the system. A simple experimental realisation of the RLAS system was implemented to investigate the degree of artefact attenuation that can be achieved via RLAS. Through a series of experiments on phantoms and human subjects, it is shown here that RLAS significantly reduces the GA, pulse (PA) and motion (MA) artefacts, while allowing accurate recording of neuronal signals. The results indicate that RLAS generally outperforms the standard artefact correction method, average artefact subtraction (AAS), in the removal of the GA and PA when motion is present, while the combination of RLAS and AAS always produces higher artefact attenuation than AAS alone. Additionally, this work demonstrates that RLAS greatly attenuates the unpredictable and highly variable MA that are very hard to remove using post-processing methods

Simultaneous EEG-fMRI : novel methods for EEG artefacts reduction at source

This thesis describes the development and application of novel techniques to reduce the EEG artefacts at source during the simultaneous acquisition of EEG and fMRI data. The work described in this thesis was carried out by the author in the Sir Peter Mansfield Magnetic Resonance Centre, School of Physics & Astronomy at the University of Nottingham, between October 2010 and January 2013. Large artefacts compromise EEG data quality during simultaneous fMRI. These artefact voltages pose heavy demands on the bandwidth and dynamic range of EEG amplifiers and mean that even small fractional variations in the artefact voltages give rise to significant residual artefacts after correction, which can easily swamp signals from brain activity. Therefore any intrinsic reduction in the magnitude of the artefacts would be highly advantageous, allowing data with a higher bandwidth to be acquired without amplifier saturation, and facilitating improved detection of brain activity. This thesis firstly explores a new method for reducing the gradient artefact (GA), which is induced in EEG data recorded during concurrent MRI, by investigating the effects of the cable configuration on the characteristics of the GA. This work showed that the GA amplitude and its sensitivity to movement of the cabling is reduced by minimising wire loop areas in the cabling between the EEG cap and amplifier. Another novel approach for reducing the magnitude and variability of the artefacts is the use of an EEG cap that incorporates electrodes embedded in a reference layer, which has a similar conductivity to tissue and is electrically isolated from the scalp. With this arrangement, the artefact voltages produced on the reference layer leads are theoretically similar to those induced in the scalp leads, but neuronal signals are not detected in the reference layer. Therefore taking the difference of the voltages in the reference and scalp channels should reduce the artefacts, without affecting sensitivity to neuronal signals. The theoretical efficacy of artefact correction that can be achieved by using this new reference layer artefact subtraction (RLAS) method was investigated. This was done through separate electromagnetic simulations of the artefacts induced in a hemispherical reference layer and a spherical volume conductor in a time-varying magnetic field and the results showed that similar artefacts are induced on the surface of both conductors. Simulations are also performed to find the optimal design for an RLAS system, by varying the geometry of the system. A simple experimental realisation of the RLAS system was implemented to investigate the degree of artefact attenuation that can be achieved via RLAS. Through a series of experiments on phantoms and human subjects, it is shown here that RLAS significantly reduces the GA, pulse (PA) and motion (MA) artefacts, while allowing accurate recording of neuronal signals. The results indicate that RLAS generally outperforms the standard artefact correction method, average artefact subtraction (AAS), in the removal of the GA and PA when motion is present, while the combination of RLAS and AAS always produces higher artefact attenuation than AAS alone. Additionally, this work demonstrates that RLAS greatly attenuates the unpredictable and highly variable MA that are very hard to remove using post-processing methods

DICOM for EIT

With EIT starting to be used in routine clinical practice [1], it important that the clinically relevant information is portable between hospital data management systems. DICOM formats are widely used clinically and cover many imaging modalities, though not specifically EIT. We describe how existing DICOM specifications, can be repurposed as an interim solution, and basis from which a consensus EIT DICOM ‘Supplement’ (an extension to the standard) can be writte

Estimation of thorax shape for forward modelling in lungs EIT

The thorax models for pre-term babies are developed based on the CT scans from new-borns and their effect on image reconstruction is evaluated in comparison with other available models