Fully automatic peak frequency estimation of the posterior dominant rhythm in a large retrospective hospital EEG cohort

Zibrandtsen, Ivan C; Kjær, Troels W.

doi:10.1016/j.cnp.2020.11.001

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…A single significant cluster identified stronger spectral magnitude in female participants between 1 and 48 Hz, peaking around the alpha and beta frequency ranges (Figure 9A). Increased power in females relative to males has been previously reported in the EEG literature (Aurlien et al, 2004;Zibrandtsen and Kjaer, 2021). Further work is required to distinguish whether this is a true neuronal difference or reflects simpler anatomical differences such as skull thickness.…”

Section: Group-level Covariate Effectsmentioning

confidence: 80%

The GLM-Spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling

Quinn

Atkinson

Gohil

et al. 2022

Preprint

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Spectrum estimators that make use of averaging across time segments are ubiquitous across neuroscience. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling and significance testing via non-parametric statistics.We apply the approach to a first-level EEG resting-state dataset alternating between eyes open and eyes closed resting-state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole-head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multi-level analysis of power spectra, with adaptive covariance and confound modelling.

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Section: Group-level Covariate Effectsmentioning

confidence: 80%

The GLM-Spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling

Quinn

Atkinson

Gohil

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…The thresholding methods rely on the assumption that the objects to segment are distinctive in their grey levels and use the histogram information, thus ignoring the spatial arrangement of pixels. Whilst these techniques are probably the most basic ones, they are still widely used [ 34 , 35 , 36 ]. Iterative pixel classifications, such as Markov random fields (MRFs) rely on the spatial interrelation of the pixels with its neighbors.…”

Section: Introductionmentioning

confidence: 99%

Impact of Training Data, Ground Truth and Shape Variability in the Deep Learning-Based Semantic Segmentation of HeLa Cells Observed with Electron Microscopy

2023

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This paper investigates the impact of the amount of training data and the shape variability on the segmentation provided by the deep learning architecture U-Net. Further, the correctness of ground truth (GT) was also evaluated. The input data consisted of a three-dimensional set of images of HeLa cells observed with an electron microscope with dimensions 8192×8192×517. From there, a smaller region of interest (ROI) of 2000×2000×300 was cropped and manually delineated to obtain the ground truth necessary for a quantitative evaluation. A qualitative evaluation was performed on the 8192×8192 slices due to the lack of ground truth. Pairs of patches of data and labels for the classes nucleus, nuclear envelope, cell and background were generated to train U-Net architectures from scratch. Several training strategies were followed, and the results were compared against a traditional image processing algorithm. The correctness of GT, that is, the inclusion of one or more nuclei within the region of interest was also evaluated. The impact of the extent of training data was evaluated by comparing results from 36,000 pairs of data and label patches extracted from the odd slices in the central region, to 135,000 patches obtained from every other slice in the set. Then, 135,000 patches from several cells from the 8192×8192 slices were generated automatically using the image processing algorithm. Finally, the two sets of 135,000 pairs were combined to train once more with 270,000 pairs. As would be expected, the accuracy and Jaccard similarity index improved as the number of pairs increased for the ROI. This was also observed qualitatively for the 8192×8192 slices. When the 8192×8192 slices were segmented with U-Nets trained with 135,000 pairs, the architecture trained with automatically generated pairs provided better results than the architecture trained with the pairs from the manually segmented ground truths. This suggests that the pairs that were extracted automatically from many cells provided a better representation of the four classes of the various cells in the 8192×8192 slice than those pairs that were manually segmented from a single cell. Finally, the two sets of 135,000 pairs were combined, and the U-Net trained with these provided the best results.

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“…Individualized Alpha Frequency (IAF) Estimates. To estimate phases for delivering phaselocked sounds, we estimated each subjects individualized alpha frequency (IAF) which is known to vary across individual adult subjects IAF (Haegens et al, 2014;Zibrandtsen & Kjaer 2020).…”

Section: Methodsmentioning

confidence: 99%

“…To estimate phases for delivering phase-locked sounds, we estimated each subjects IAF which is known to vary across individual adult subjects IAF ( Haegens et al, 2014 ; Zibrandtsen and Kjaer 2020 ). The 1/ f aperiodic component of the corresponding power spectral density (PSD) plot was fitted with a third-order polynomial and subtracted to determine the frequency with maximum power or IAF ( Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, our group and others observe short latency auditory evoked responses at frontal cortical locations in primates including humans ( Knight et al, 1999 ; Marrouch et al, 2020 ). Here, we target alpha phases that account for natural variations in individualized alpha frequency (IAF; Haegens et al, 2014 ; Zibrandtsen and Kjaer 2020 ) and auditory ERP latencies across subjects ( Prakash et al, 2016 ). Post hoc analysis confirms our approach effectively plays sounds at targeted trough and peak alpha phases which in turn aligns the early auditory evoked P1 potential with the following peak and trough phases, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Individualized Closed-Loop Acoustic Stimulation Suggests an Alpha Phase Dependence of Sound Evoked and Induced Brain Activity Measured with EEG Recordings

Harlow,

Marquez,

Bressler

et al. 2024

eNeuro

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Following repetitive visual stimulation, post-hoc phase analysis finds that visual evoked response magnitudes vary with the cortical alpha oscillation phase that temporally coincides with sensory stimulus. In the auditory system, this approach has not successfully revealed an alpha phase-dependence of auditory evoked or induced responses. Here, we test feasibility to track alpha with scalp electroencephalogram (EEG) recordings and play sounds phase-locked to individualized alpha phases in real-time using a novel end-point corrected Hilbert transform (ecHT) algorithm implemented on a research device. Based on prior work, we hypothesize that sound evoked and induced responses vary with the alpha phase at sound onset and the alpha phase that coincides with the early sound evoked response potential (ERP) measured with EEG. Thus, we use each subject's individualized alpha frequency (IAF) and individual auditory ERP latency to define target trough and peak alpha phases that allow an early component of the auditory ERP to align to estimated post-stimulus peak and trough phases, respectively. With this closed-loop and individualized approach, we find opposing alpha phase-dependent effects on the auditory ERP and alpha oscillations that follow stimulus onset. Trough and peak phase-locked sounds result in distinct evoked and induced modulations of post-stimulus alpha levels and frequency. Though additional studies are needed to localize the sources underlying these phase-dependent effects, these results suggest a general principle for alpha phase-dependence of sensory processing that includes the auditory system. Moreover, this study demonstrates feasibility to use individualized neurophysiological indices to deliver automated, closed-loop, phase-locked auditory stimulation.Significance statementHealthy adult brains generate alpha oscillations and individual subjects have different alpha oscillation frequencies, which impacts how they dynamically process and attend to sensory information. Yet, little is known about the fine scale temporal dynamics between sensory events and alpha phase and corresponding neuromodulation of auditory input processing. Here we use a novel closed-loop technology and individualized approach to play sounds at specific frontal alpha phases. We demonstrate novel alpha phase-dependent effects on auditory evoked responses, alpha levels, alpha phase-coherence and frequency. This individually tailored closed-loop approach has potential applications for research and health applications for a variety of neurological, developmental and clinical disorders.

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Fully automatic peak frequency estimation of the posterior dominant rhythm in a large retrospective hospital EEG cohort

Abstract: Highlights Fully automatic estimation of the peak frequency of the posterior dominant rhythm. Automatic estimates are very similar to human ratings. Algorithm made available with a simple graphical interface.

Cited by 5 publications

References 29 publications

The GLM-Spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling

The GLM-Spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling

Impact of Training Data, Ground Truth and Shape Variability in the Deep Learning-Based Semantic Segmentation of HeLa Cells Observed with Electron Microscopy

Individualized Closed-Loop Acoustic Stimulation Suggests an Alpha Phase Dependence of Sound Evoked and Induced Brain Activity Measured with EEG Recordings

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