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The phase slips are generally extracted from the EEG using Hilbert transforms but could also be extracted from the derivatives of EEG, providing additional information about the formation of cortical phase transitions. We examined this from the 30 s long, 256-channel resting state, eyes open EEG data of a 30-year-old male subject. The phase slip rates, PSR1 from EEG, PSR2 from the first-order derivative of EEG, and PSR3 from the second-order derivative of EEG, respectively, were extracted. The study was performed in the alpha (7–12 Hz) band only. The spatiotemporal plots of the EEG and phase slip rates over a 3.0 s period with a 0.5 s resolution were made with a montage layout of the 256 electrode positions. The spatiotemporal patterns of EEG and its derivatives exhibited shifting activity from posterior visual areas to the central and frontal regions over the 3.0 s period. The PSR1, PSR2, and PSR3 activity areas were different from the EEG and were distributed in larger areas as compared with the EEG and its derivatives. Also, the PSR2 and PSR3 activity areas and magnitudes were significantly different as compared with the PSR1 alone. This was also confirmed ( p  < 0.01) by the one-way ANOVA analysis of the means of PSR1, PSR2, and PSR3. These results show that PSR2 and PSR3 carry additional information that could potentially be biomarkers for studying the rate of formation of phase slips and the related cortical activity from the derivatives of EEG data.

We found that phase cone clustering patterns in EEG ripple bands demonstrate an increased turnover rate in epileptogenic zones compared to adjacent regions. We employed 256 channel EEG data collected in four adult subjects with refractory epilepsy. The analysis was performed in the 80–150 and 150–250 Hz ranges. Ictal onsets were documented with intracranial EEG recordings. Interictal scalp recordings, free of epileptiform patterns, of 240-s duration, were selected for analysis for each subject. The data was filtered, and the instantaneous phase was extracted after the Hilbert transformation. Spatiotemporal contour plots of the unwrapped instantaneous phase with 1.0 ms intervals were constructed using a montage layout of the 256 electrode positions. Stable phase cone patterns were selected based on criteria that the sign of spatial gradient did not change for a minimum of three consecutive time samples and the frame velocity was consistent with known propagation velocities of cortical axons. These plots exhibited increased dynamical formation and dissolution of phase cones in the ictal onset zones, compared to surrounding cortical regions, in all four patients. We believe that these findings represent markers of abnormally increased cortical excitability. They are potential tools that may assist in localizing the epileptogenic zone.

Phase slips arise from state transitions of the coordinated activity of cortical neurons which can be extracted from the EEG data. The phase slip rates (PSRs) were studied from the high-density (256 channel) EEG data, sampled at 16.384 kHz, of five adult subjects during covert visual object naming tasks. Artifact-free data from 29 trials were averaged for each subject. The analysis was performed to look for phase slips in the theta (4–7 Hz), alpha (7–12 Hz), beta (12–30 Hz), and low gamma (30–49 Hz) bands. The phase was calculated with the Hilbert transform, then unwrapped and detrended to look for phase slip rates in a 1.0 ms wide stepping window with a step size of 0.06 ms. The spatiotemporal plots of the PSRs were made by using a montage layout of 256 equidistant electrode positions. The spatiotemporal profiles of EEG and PSRs during the stimulus and the first second of the post-stimulus period were examined in detail to study the visual evoked potentials and different stages of visual object recognition in the visual, language, and memory areas. It was found that the activity areas of PSRs were different as compared with EEG activity areas during the stimulus and post-stimulus periods. Different stages of the insight moments during the covert object naming tasks were examined from PSRs and it was found to be about 512 ± 21 ms for the ‘Eureka’ moment. Overall, these results indicate that information about the cortical phase transitions can be derived from the measured EEG data and can be used in a complementary fashion to study the cognitive behavior of the brain.

We examined the effects of slow‐pulsed transcranial electrical stimulation (TES) in suppressing epileptiform discharges in seven adults with refractory epilepsy. An MRI‐based realistic head model was constructed for each subject and co‐registered with 256‐channel dense EEG (dEEG). Interictal spikes were localized, and TES targeted the cortical source of each subject's principal spike population. Targeted spikes were suppressed in five subject's (29/35 treatment days overall), and nontargeted spikes were suppressed in four subjects. Epileptiform activity did not worsen. This study suggests that this protocol, designed to induce long‐term depression (LTD), is safe and effective in acute suppression of interictal epileptiform discharges.

This paper reviews the novel use of CT and MRI data and image processing tools to segment and reconstruct tissue images in 3D to determine characteristics of muscle, bone and brain.This to study and simulate the structural changes occurring in healthy and pathological conditions as well as in response to clinical treatments. Here we report the application of this methodology to evaluate and quantify: 1. progression of atrophy in human muscle subsequent to permanent lower motor neuron (LMN) denervation, 2. muscle recovery as induced by functiol electrical stimulation (FES), 3. bone quality in patients undergoing total hip replacement and 4. to model the electrical activity of the brain. Study 1: CT data and segmentation techniques were used to quantify changes in muscle density and composition by associating the Hounsfield unit values of muscle, adipose and fibrous connective tissue with different colors. This method was employed to monitor patients who have permanent muscle LMN denervation in the lower extremities under two different conditions: permanent LMN denervated not electrically stimulated and stimulated. Study 2: CT data and segmentation techniques were employed, however, in this work we assessed bone and muscle conditions in the pre-operative CT scans of patients scheduled to undergo total hip replacement. In this work, the overall atomical structure, the bone mineral density (BMD) and compactness of quadriceps muscles and proximal femoral was computed to provide a more complete view for surgeons when deciding which implant technology to use. Further, a Finite element alysis provided a map of the strains around the proximal femur socket when solicited by typical stresses caused by an implant press fitting. Study 3 describes a method to model the electrical behavior of human brain using segmented MR images. The aim of the work is to use these models to predict the electrical activity of the human brain under normal and pathological conditions by developing detailed 3D representations of major tissue surfaces within the head, with over 12 different tissues segmented. In addition, computatiol tools in Matlab were developed for calculating normal vectors on the brain surface and for associating this information with the equivalent electrical dipole sources as an input into the model.

Effects of soft skull bone, cerebrospinal fluid (CSF) and gray matter on scalp potentials were examined with highly heterogeneous finite element models of an adult male subject. These models were constructed from segmented T1 weighted magnetic resonance images. Models had voxel resolutions of 1x1x3.2 mm with a total of about 1.5 million voxels. The scalp potentials, due to a dipolar source in the motor cortex area, were computed with an adaptive finite element solver. It was found that the scalp potentials were significantly affected by the soft bone, CSF and gray matter tissue boundaries in the models.

Improving EEG sigl interpretation, specificity, and sensitivity is a primary focus of many current investigations, and the successful application of EEG sigl processing methods requires a detailed knowledge of both the topography and frequency spectra of low-amplitude, high-frequency craniofacial EMG. This information remains limited in clinical research, and as such, there is no known reliable technique for the removal of these artifacts from EEG data. The results presented herein outline a prelimiry investigation of craniofacial EMG high-frequency spectra and 3D MRI segmentation that offers insight into the development of an atomically-realistic model for characterizing these effects. The data presented highlights the potential for confounding sigl contribution from around 60 to 200 Hz, when observed in frequency space, from both low and high-amplitude EMG sigls. This range directly overlaps that of both low γ (30-50 Hz) and high γ (50-80 Hz) waves, as defined traditiolly in standatrd EEG measurements, and mainly with waves presented in dense-array EEG recordings. Likewise, average EMG amplitude comparisons from each condition highlights the similarities in sigl contribution of low-activity muscular movements and resting, control conditions. In addition to the FFT alysis performed, 3D segmentation and reconstruction of the craniofacial muscles whose EMG sigls were measured was successful. This recapitulation of the relevant EMG morphology is a crucial first step in developing an atomical model for the isolation and removal of confounding low-amplitude craniofacial EMG sigls from EEG data. Such a model may be eventually applied in a clinical setting to ultimately help to extend the use of EEG in various clinical roles.

This paper reviews the novel use of CT and MRI data and image processing tools to segment and reconstruct tissue images in 3D to determine characteristics of muscle, bone and brain.This to study and simulate the structural changes occurring in healthy and pathological conditions as well as in response to clinical treatments. Here we report the application of this methodology to evaluate and quantify: 1. progression of atrophy in human muscle subsequent to permanent lower motor neuron (LMN) denervation, 2. muscle recovery as induced by functional electrical stimulation (FES), 3. bone quality in patients undergoing total hip replacement and 4. to model the electrical activity of the brain. Study 1: CT data and segmentation techniques were used to quantify changes in muscle density and composition by associating the Hounsfield unit values of muscle, adipose and fibrous connective tissue with different colors. This method was employed to monitor patients who have permanent muscle LMN denervation in the lower extremities under two different conditions: permanent LMN denervated not electrically stimulated and stimulated. Study 2: CT data and segmentation techniques were employed, however, in this work we assessed bone and muscle conditions in the pre-operative CT scans of patients scheduled to undergo total hip replacement. In this work, the overall anatomical structure, the bone mineral density (BMD) and compactness of quadriceps muscles and proximal femoral was computed to provide a more complete view for surgeons when deciding which implant technology to use. Further, a Finite element analysis provided a map of the strains around the proximal femur socket when solicited by typical stresses caused by an implant press fitting. Study 3 describes a method to model the electrical behavior of human brain using segmented MR images. The aim of the work is to use these models to predict the electrical activity of the human brain under normal and pathological conditions by developing detailed 3D representations of major tissue surfaces within the head, with over 12 different tissues segmented. In addition, computational tools in Matlab were developed for calculating normal vectors on the brain surface and for associating this information with the equivalent electrical dipole sources as an input into the model.

There is an increasing demand for source analysis of neonatal EEG, but currently there is inadequate knowledge about i) the spatial patterning of neonatal scalp EEG and hence ii) the number of electrodes needed to capture neonatal EEG in full spatial detail. This study addresses these issues by using a very high density (2.5mm interelectrode spacing) linear electrode array to assess the spatial power spectrum, by using a high density (64 electrodes) EEG cap to assess the spatial extent of the common oscillatory bouts in the neonatal EEG and by using a neonatal size spherical head model to assess the effects of source depth and skull conductivities on the spatial frequency spectrum. The linear array recordings show that the spatial power spectrum decays rapidly until about 0.5–0.8cycles per centimeter. The dense array EEG recordings show that the amplitude of oscillatory events decays within 4–6cm to the level of global background activity, and that the higher frequencies (12–20Hz) show the most rapid spatial decline in amplitude. Simulation with spherical head model showed that realistic variation in skull conductivity and source depths can both introduce orders of magnitude difference in the spatial frequency of the scalp EEG. Calculation of spatial Nyquist frequencies from the spatial power spectra suggests that an interelectrode distance of about 6–10mm would suffice to capture the full spatial texture of the raw EEG signal at the neonatal scalp without spatial aliasing or under-sampling. The spatial decay of oscillatory events suggests that a full representation of their spatial characteristics requires an interelectrode distance of 10–20mm. The findings show that the conventional way of recording neonatal EEG with about 10 electrodes ignores most spatial EEG content, that increasing the electrode density is necessary to improve neonatal EEG source localization and information extraction, and that prospective source models will need to carefully consider the neonatally relevant ranges of tissue conductivities and source depths when source localizing cortical activity in neonates. ► The spatial patterning of scalp EEG is very dense in neonates. ► Oscillatory events in the neonatal scalp EEG are spatially confined. ► An optimal neonatal EEG recording would require significantly more electrodes.