21,144 research outputs found
Listening for speaking: Investigations of the relationship between speech perception and production
No full textContains fulltext :
180052.pdf (Publisher’s version ) (Open Access)Radboud University, 05 februari 2018Promotores : Hagoort, P., McQueen, J.M. Co-promotores : Acheson, D.J., Schoffelen, J.M.225 p
FieldTrip: the MATLAB software toolbox for MEG, EEG and iEEG analysis
No full text<p>FieldTrip is the MATLAB software toolbox for MEG, EEG and iEEG analysis, which is released free of charge as <a href="https://en.wikipedia.org/wiki/Open_source">open source software</a> under the GNU <a href="https://www.gnu.org/copyleft/gpl.html">general public license</a>. FieldTrip is developed by members and collaborators of the <a href="https://www.ru.nl/donders/">Donders Institute for Brain, Cognition and Behaviour</a> at <a href="https://www.ru.nl/">Radboud University</a>, Nijmegen, the Netherlands.</p>
<p>This release corresponds to the 2010 end-of-year version of the FieldTrip toolbox.</p><p>If you use this software, please cite Robert Oostenveld, Pascal Fries, Eric Maris, and Jan-Mathijs Schoffelen. <strong><a href="https://doi.org/10.1155/2011/156869">FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data.</a></strong> <em>Computational Intelligence and Neuroscience, 2011; 2011:156869.</em></p>
FieldTrip: the MATLAB software toolbox for MEG, EEG and iEEG analysis
No full text<p>FieldTrip is the MATLAB software toolbox for MEG, EEG and iEEG analysis, which is released free of charge as <a href="https://en.wikipedia.org/wiki/Open_source">open source software</a> under the GNU <a href="https://www.gnu.org/copyleft/gpl.html">general public license</a>. FieldTrip is developed by members and collaborators of the <a href="https://www.ru.nl/donders/">Donders Institute for Brain, Cognition and Behaviour</a> at <a href="https://www.ru.nl/">Radboud University</a>, Nijmegen, the Netherlands.</p>
<p>This release corresponds to the 2021 end-of-year version of the FieldTrip toolbox.</p><p>If you use this software, please cite Robert Oostenveld, Pascal Fries, Eric Maris, and Jan-Mathijs Schoffelen. <strong><a href="https://doi.org/10.1155/2011/156869">FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data.</a></strong> <em>Computational Intelligence and Neuroscience, 2011; 2011:156869.</em></p>
FieldTrip: the MATLAB software toolbox for MEG, EEG and iEEG analysis
No full text<p>FieldTrip is the MATLAB software toolbox for MEG, EEG and iEEG analysis, which is released free of charge as <a href="https://en.wikipedia.org/wiki/Open_source">open source software</a> under the GNU <a href="https://www.gnu.org/copyleft/gpl.html">general public license</a>. FieldTrip is developed by members and collaborators of the <a href="https://www.ru.nl/donders/">Donders Institute for Brain, Cognition and Behaviour</a> at <a href="https://www.ru.nl/">Radboud University</a>, Nijmegen, the Netherlands.</p>
<p>This release corresponds to the 2009 end-of-year version of the FieldTrip toolbox.</p><p>If you use this software, please cite Robert Oostenveld, Pascal Fries, Eric Maris, and Jan-Mathijs Schoffelen. <strong><a href="https://doi.org/10.1155/2011/156869">FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data.</a></strong> <em>Computational Intelligence and Neuroscience, 2011; 2011:156869.</em></p>
FieldTrip: the MATLAB software toolbox for MEG, EEG and iEEG analysis
No full text<p>FieldTrip is the MATLAB software toolbox for MEG, EEG and iEEG analysis, which is released free of charge as <a href="https://en.wikipedia.org/wiki/Open_source">open source software</a> under the GNU <a href="https://www.gnu.org/copyleft/gpl.html">general public license</a>. FieldTrip is developed by members and collaborators of the <a href="https://www.ru.nl/donders/">Donders Institute for Brain, Cognition and Behaviour</a> at <a href="https://www.ru.nl/">Radboud University</a>, Nijmegen, the Netherlands.</p>
<p>This release corresponds to the 2011 end-of-year version of the FieldTrip toolbox.</p><p>If you use this software, please cite Robert Oostenveld, Pascal Fries, Eric Maris, and Jan-Mathijs Schoffelen. <strong><a href="https://doi.org/10.1155/2011/156869">FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data.</a></strong> <em>Computational Intelligence and Neuroscience, 2011; 2011:156869.</em></p>
Using Probabilistic Language Models for Tracking Modulations in MEG Spectral Power During Auditory Narrative Comprehension
No full textThis study set to establish a direct link between formally-modelled predictive language comprehension processes and scalp-recorded electrophysiological signals. We recorded MEG while participants listened to 4–8 minutes long auditory stories (narratives) with no secondary linguistic task. Predictive language comprehension was modeled with probabilistic language models. On the basis of language-model output, two information-theoretic complexity metrics, word surprisal and word entropy, were computed word-by-word for all stories and correlated with modulations of MEG power envelopes in the theta (4-8 Hz) and beta (12-18 Hz) frequency bands. We used the framework of mutual information analysis to quantify the strength of statistical relationship between linguistic and MEG signals. In this preliminary analysis, we were not able to confirm any significant statistical relationship between either word entropy or word surprisal and theta- or beta-band MEG signals. To confirm that mutual information as implemented in our analysis could otherwise reveal meaningful statistical relationships in our signals, we show that there was stronger audio-MEG phase alignment in the theta than in the beta frequency band. We conclude by evaluating the current approach and outline possible avenues for follow-up research
Deconvolution of broad-band responses to a transient response explains steady-steady respo
No full textElectroencephalographic evoked responses can be divided in at least three classes; transient responses, steady-state responses, and broad-band responses. According to the classic view, the underlying neural mechanisms of evoked responses di˙er substantially. However, several recent studies have found evidence suggesting a common mechanism explaining steady-state responses by a superposition of a transient response. In this study, we investigated the superposition hypothesis in the visual domain. We estimated the transient response to a single flash using a generative linear framework. This was achieved by a deconvolution of broad-band responses. From this transient response, we generated synthetic steady-state and broad-band responses by summation of time-shifted versions of this transient response. We were able to obtain a portion of explained variance of 0.56, on average. We did not find appreciable oscillatory activity in the residuals. Therefore, steady-state and broad-band responses can be understood as linear phenomenon, in line with the superposition hypothesis
Towards a more robust non-invasive assessment of functional connectivity
No full textNon-invasive evaluation of functional connectivity, based on source-reconstructed estimates of phase-difference-based metrics, is notoriously non-robust. This is due to a combination of factors, ranging from a misspecification of seed regions to suboptimal baseline assumptions, and residual signal leakage. In this work, we propose a new analysis scheme of source level phase-difference-based connectivity, which is aimed at optimizing the detection of interacting brain regions. Our approach is based on the combined use of sensor subsampling and dual-source beamformer estimation of all-to-all connectivity on a prespecified dipolar grid. First, a pairwise two-dipole model, to account for reciprocal leakage in the estimation of the localized signals, allows for a usable approximation of the pairwise bias in connectivity due to residual leakage of ‘third party’ noise. Secondly, using sensor array subsampling, the recreation of multiple connectivity maps using different subsets of sensors allows for the identification of consistent spatially localized peaks in the 6-dimensional connectivity maps, indicative of true brain region interactions. These steps are combined with the subtraction of null coherence estimates to obtain the final coherence maps. With extensive simulations, we compared different analysis schemes for their detection rate of connected dipoles, as a function of signal-to-noise ratio, phase difference and connection strength. We demonstrate superiority of the proposed analysis scheme in comparison to single-dipole models, or an approach that discards the zero phase difference component of the connectivity. We conclude that the proposed pipeline allows for a more robust identification of functional connectivity in experimental data, opening up new possibilities to study brain networks with mechanistically inspired connectivity measures in cognition and in the clinic.peerReviewe
Using brain potentials to functionally localise Stroop-like effects in colour and picture naming: Perceptual encoding versus word planning
No full textThis dataset belongs to the research article entitled "Using brain potentials to functionally localise Stroop-like effects in colour and picture naming: Perceptual encoding versus word planning", authored by Natalia Shitova, Ardi Roelofs, Herbert Schriefers, Marcel Bastiaansen, and Jan-Mathijs Schoffelen, accepted for publication in PLOS ONE.
The experiment was performed in 24 human subjects, using behavioural measures, such as reaction time and error rate, and scalp EEG recordings. This dataset includes behavioural and EEG raw data for three tasks (the classical Stroop task, the classical Picture-word Interference task, and a Stroop-like Picture-Word Interference task), as well as presentation scripts used during the experiment (in Presentation software) and analysis scripts (in Matlab). The data and scripts given are sufficient to replicate findings described in the article
Listeners Normalise to Speaker Rate Dynamics Irrespective of Selective Attention
No full textTemporal characteristics are fundamental to speech perception. This perception is often relative to contextual information rather than absolute. For example, surrounding speech context contrastively modulates the perception of subsequent words, known as context effect. This normalisation to the temporal properties of contextual speech appears to be supported by entrainment of neural oscillations to fast vs. slow syllabic rhythms. Moreover, we often find ourselves surrounded by multiple speakers, requiring that we “tune in” to relevant speech while inhibiting attention towards distracting speakers. So, do listeners normalise words only for the speech rate of an attended talker, or does an unattended speech rate also influence speech perception? Further, is there a relationship between successful attention and modulation of these context effects? In a magnetoencephalography study, participants were instructed to attend to one of two dichotically presented, rate matched or mismatched sentences. Following this, they categorised ambiguous target words. As a neural signature of success in selective auditory attention we computed an alpha (~10 Hz) power lateralisation index. Context effects were found following matching rates but not mismatching rates, suggesting that rate normalisation factors in the global listening environment. Further, our findings support previous research implementing alpha lateralisation as a neural index of attentional demands rather than successful attention. The findings herein contribute to our understanding of the properties of speech that precede attentional stream segregation. This in turn, could contribute to the development of modern hearing aids that are already attempting to take advantage of electrophysiological research methods
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