MNE: Software for Acquiring, Processing, and Visualizing MEG/EEG Data

Esch, Lorenz; Dinh, Christoph; Larson, Eric; Engemann, Denis A.; Jas, Mainak; Khan, Sheraz; Gramfort, Alexandre; Hämäläinen, Matti S.

doi:10.1007/978-3-030-00087-5_59

Cited by 21 publications

(10 citation statements)

References 64 publications

(49 reference statements)

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“…The regularized (λ = 0.1) noise covariance matrix used to calculate the inverse operator was calculated over the pre-stimulus period. All forward and inverse calculations were done using MNE-C and MNE-python software ( Gramfort et al, 2013a ; Esch et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks

et al. 2021

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Most magneto- and electroencephalography (M/EEG) based source estimation techniques derive their estimates sample wise, independently across time. However, neuronal assemblies are intricately interconnected, constraining the temporal evolution of neural activity that is detected by MEG and EEG; the observed neural currents must thus be highly context dependent. Here, we use a network of Long Short-Term Memory (LSTM) cells where the input is a sequence of past source estimates and the output is a prediction of the following estimate. This prediction is then used to correct the estimate. In this study, we applied this technique on noise-normalized minimum norm estimates (MNE). Because the correction is found by using past activity (context), we call this implementation Contextual MNE (CMNE), although this technique can be used in conjunction with any source estimation method. We test CMNE on simulated epileptiform activity and recorded auditory steady state response (ASSR) data, showing that the CMNE estimates exhibit a higher degree of spatial fidelity than the unfiltered estimates in the tested cases.

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Section: Methodsmentioning

confidence: 99%

Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks

et al. 2021

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“…fNIRS data was preprocessed and analyzed using a custom script based on free software packages MNE-fNIRS (Esch et al, 2019;Luke et al, 2020). MNE provides complete data analysis pipelines and toolboxes in Python for fNIRS data processing.…”

Section: Statistical Analysis Of Nirs Datamentioning

confidence: 99%

The Neural Correlates of Exact Calculation in Word and Numerical Formats in Low And High Math Performers: A fNIRS Study

Sitnikova

Marakshina

Adamovich

et al. 2023

IJCRSEE

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The representation format of math problems can manifest dissimilarly in people with varying levels of math performance. The aim of this study was to investigate the neurocognitive underpinnings of two-digit exact calculation tasks in different formats: numerical and word. Fifty-three students were divided into groups with high and low levels of math performance based on their ability to solve math problems. They were asked to calculate addition problems with two-digit numbers and to read math problems that did not require calculations. Brain activation was recorded using functional near-infrared spectroscopy (fNIRS). General linear model (GLM) analysis revealed that reading math problems without calculation led to increased activation in the inferior temporal and fusiform gyri in a group of high-level performers, while a group of low-level performers demonstrated increased activation in Broca’s area and the inferior frontal gyrus in the same experimental condition, as well as during solving arithmetic problems in a word format of the exact calculation task. Analysis after bootstrapping revealed similar activation patterns in both groups. Both domain-specific and domain-general regions of the frontal and parietal brain areas were involved in the calculations. Right and left hemisphere activation was found both in low and high math performers. Comparing experimental conditions with resting state revealed significant activation in Broca’s area in all conditions in a group of high-level performers and in a word format of arithmetic problems in a group of low-level performers. Thus, the observed brain patterns suggest the involvement of complex sentence comprehension, especially in high-performing students. These results could be used in future to improve educational practice for students with varying levels of math competence.

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“…Further details regarding the architecture of MNE-CPP can be found in the reference publication (Esch et al, 2019) and on https://mne-cpp. github.io/.…”

Section: Mne-cppmentioning

confidence: 99%

Online functional connectivity analysis of large all-to-all networks

Esch¹,

Dong²,

Hämäläinen³

et al. 2023

Preprint

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The analysis of EEG/MEG functional connectivity has become an important tool in neural research. Especially the high time resolution of EEG/MEG enables important insight into the functioning of the human brain. To date, functional connectivity is commonly estimated offline, i.e., after the conclusion of the experiment. However, online computation of functional connectivity has the potential to enable unique experimental paradigms. For example, changes of functional connectivity due to learning processes could be tracked in real time and the experiment be adjusted based on these observations. Furthermore, the connectivity estimates can be used for neurofeedback applications or the instantaneous inspection of measurement results. In this study, we present the implementation and evaluation of online sensor and source space functional connectivity estimation in the open-source software MNE Scan. Online capable implementations of several functional connectivity metrics were established in the Connectivity library within MNE-CPP and made available as a plugin in MNE Scan. Online capability was achieved

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MNE: Software for Acquiring, Processing, and Visualizing MEG/EEG Data

Cited by 21 publications

References 64 publications

Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks

Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks

The Neural Correlates of Exact Calculation in Word and Numerical Formats in Low And High Math Performers: A fNIRS Study

Online functional connectivity analysis of large all-to-all networks

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