Single-trial reconstruction of auditory evoked magnetic fields by means of Template Matching Pursuit

Jörn, M.; Sielużycki, Cezary; Matysiak, M.A.; Żygierewicz, Jarosław; Scheich, Henning; Durka, P.J.; König, Reinhard

doi:10.1016/j.jneumeth.2011.04.019

Cited by 10 publications

(8 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…if the responses can be expected to habituate over trials (e.g. Ritter et al ., ; Callaway, ; Prosser et al ., ; Sielużycki et al ., ; Jörn et al ., ) or change otherwise. We therefore recommend scrutinizing the data for compliance with the additive model.…”

Section: Discussionmentioning

confidence: 99%

Averaging auditory evoked magnetoencephalographic and electroencephalographic responses: a critical discussion

König

Matysiak

Kordecki

et al. 2015

Eur J of Neuroscience

View full text Add to dashboard Cite

In the analysis of data from magnetoencephalography (MEG) and electroencephalography (EEG), it is common practice to arithmetically average event-related magnetic fields (ERFs) or event-related electric potentials (ERPs) across single trials and subsequently across subjects to obtain the so-called grand mean. Comparisons of grand means, e.g. between conditions, are then often performed by subtraction. These operations, and their statistical evaluation with parametric tests such as ANOVA, tacitly rely on the assumption that the data follow the additive model, have a normal distribution, and have a homogeneous variance. This may be true for single trials, but these conditions are rarely met when ERFs/ERPs are compared between subjects, meaning that the additive model is seldom the correct model for computing grand mean waveforms. Here, we summarize some of our recent work and present new evidence, from auditory-evoked MEG and EEG results, that the non-normal distributions and the heteroscedasticity observed instead result because ERFs/ERPs follow a mixed model with additive and multiplicative components. For peak amplitudes, such as the auditory M100 and N100, the multiplicative component dominates. These findings emphasize that the common practice of simply subtracting arithmetic means of auditory-evoked ERFs or ERPs is problematic without prior adequate transformation of the data. Application of the area sinus hyperbolicus (asinh) transform to data following the mixed model transforms them into the requested additive model with its normal distribution and homogeneous variance. We therefore advise checking the data for compliance with the additive model and using the asinh transform if required.

show abstract

Section: Discussionmentioning

confidence: 99%

Averaging auditory evoked magnetoencephalographic and electroencephalographic responses: a critical discussion

König

Matysiak

Kordecki

et al. 2015

Eur J of Neuroscience

View full text Add to dashboard Cite

show abstract

“…An a priori is thus injected through this prototype, generally with the shift-invariant model. For example, monochannel patterns as Gaussian functions (Lange et al, 1997), time-limited sinosoids (Jaśkowski and Verleger, 1999), generic mass potentials (Melkonian et al, 2003), Gamma functions (Li et al, 2009) and Gabor functions (Jörn et al, 2011) have been used to match the P300 and other evoked potentials. Multichannel patterns have been introduced in (Gratkowski et al, 2008) using Gabor temporal atoms and multichannel coefficients based on Bessel functions which try to model the spatial dependencies between channels.…”

Section: Review Of Models and Methodsmentioning

confidence: 99%

Multivariate temporal dictionary learning for EEG

Barthélemy

Gouy-Pailler

Isaac

et al. 2013

Journal of Neuroscience Methods

View full text Add to dashboard Cite

This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential.

show abstract

“…Therefore, more mathematically advanced methods of analysis had to find their place in neuroscience [32]. Plenty of them are time-frequency analysis, with the use of tools such as wavelets or matching pursuit [7, 8, 17, 22, 25, 47, 49]. Some of them are fractal or entropy based [45, 51].…”

Section: Introductionmentioning

confidence: 99%

Time series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattented listening and frequency-modulated tones classification

Kipiński

Kordecki

2021

Preprint

View full text Add to dashboard Cite

The nonstationarity of EEG/MEG signals is important for understanding the functioning of human brain. From the previous research we know that even very short, i.e. 250-500ms MEG signals are variance-nonstationary. The covariance of stochastic process is mathematically associated with its spectral density, therefore we investigate how the spectrum of such nonstationary signals varies in time. We analyze the data from 148-channel MEG, that represent rest state, unattented listening and frequency-modulated tones classification. We transform short-time MEG signals to the frequency domain using the FFT algorithm and for the dominant frequencies 8-12Hz we prepare the time series representing their trial-to-trial variability. Then, we test them for level- and trend-stationarity, unit root, heteroscedasticity and gaussianity and based on their properties we propose the ARMA-modelling for their description. The analyzed time series have the weakly stationary properties independently of the functional state of brain and localization. Only their small percentage, mostly related to the cognitive task, still presents nonstationarity. The obtained mathematical models show that the spectral density of analyzed signals depends on only 2-3 previous trials. The presented method has limitations related to FFT resolution and univariate models, but it is not computationally complicated and allows to obtain a low-complex stochastic models of the EEG/MEG pectrum variability. Although the physiological short-time MEG signals are in principle nonstationary in time domain, its power spectrum at the dominant frequencies varies as weakly stationary stochastic process. Described technique has the possible applications in prediction of the EEG/MEG spectral properties in theoretical and clinical neuroscience.

show abstract

Single-trial reconstruction of auditory evoked magnetic fields by means of Template Matching Pursuit

Cited by 10 publications

References 40 publications

Averaging auditory evoked magnetoencephalographic and electroencephalographic responses: a critical discussion

Averaging auditory evoked magnetoencephalographic and electroencephalographic responses: a critical discussion

Multivariate temporal dictionary learning for EEG

Time series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattented listening and frequency-modulated tones classification

Contact Info

Product

Resources

About