Search results for "Eeg"
showing 10 items of 313 documents
A Meshfree Boundary Method for M/EEG Forward Computations
2014
Using On-Demand File Systems in HPC Environments
2019
In modern HPC systems, parallel (distributed) file systems are used to allow fast access from and to the storage infrastructure. However, I/O performance in large-scale HPC systems has failed to keep up with the increase in computational power. As a result, the I/O subsystem which also has to cope with a large number of demanding metadata operations is often the bottleneck of the entire HPC system. In some cases, even a single bad behaving application can be held responsible for slowing down the entire HPC system, disrupting other applications that use the same I/O subsystem. These kinds of situations are likely to become more frequent in the future with larger and more powerful HPC systems…
Multivariate correlation measures reveal structure and strength of brain–body physiological networks at rest and during mental stress
2021
In this work, we extend to the multivariate case the classical correlation analysis used in the field of network physiology to probe dynamic interactions between organ systems in the human body. To this end, we define different correlation-based measures of the multivariate interaction (MI) within and between the brain and body subnetworks of the human physiological network, represented, respectively, by the time series of delta, theta, alpha, and beta electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability. MI is computed: (i) considering all variables in the two subnetworks to evaluate overall brain–body interaction…
Extended causal modeling to assess Partial Directed Coherence in multiple time series with significant instantaneous interactions.
2010
The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. M…
Pre- and post-ictal brain activity characterization using combined source decomposition and connectivity estimation in epileptic children
2019
In this research, the study of functional connectivity between sources of electroencephalogram (EEG) activity assessed for different classes (well before seizure, preictal and post-ictal) was performed. EEG recordings were acquired from 12 subjects with focal epilepsy. Then, ten common spatial patterns (CSP) were obtained for EEG segments describing 95% of Riemannian distance between pairs of classes, followed by estimation of multivariate autoregressive (MVAR) models’ coefficients. The MVAR models were further used to extract coherence as a functional connectivity measures. Our results show that the coherence between CSP sources differs between baseline and pre-ictal segments: it has the l…
Predictive error detection in pianists: A combined ERP and motion capture study
2013
Performing a piece of music involves the interplay of several cognitive and motor processes and requires extensive training to achieve a high skill level. However, even professional musicians commit errors occasionally. Previous event-related potential (ERP) studies have investigated the neurophysiological correlates of pitch errors during piano performance, and reported pre-error negativity already occurring approximately 70–100 ms before the error had been committed and audible. It was assumed that this pre-error negativity reflects predictive control processes that compare predicted consequences with actual consequences of one's own actions. However, in previous investigations, correct a…
2014
Due to its millisecond-scale temporal resolution, EEG allows to assess neural correlates with precisely defined temporal relationship relative to a given event. This knowledge is generally lacking in data from functional magnetic resonance imaging (fMRI) which has a temporal resolution on the scale of seconds so that possibilities to combine the two modalities are sought. Previous applications combining event-related potentials (ERPs) with simultaneous fMRI BOLD generally aimed at measuring known ERP components in single trials and correlate the resulting time series with the fMRI BOLD signal. While it is a valuable first step, this procedure cannot guarantee that variability of the chosen …
Intention affects fairness processing : Evidence from behavior and representational similarity analysis of event‐related potential signals
2023
In an ultimatum game, the responder must decide between pursuing self-interest and insisting on fairness, and these choices are affected by the intentions of the proposer. However, the time course of this social decision-making process is unclear. Representational similarity analysis (RSA) is a useful technique for linking brain activity with rich behavioral data sets. In this study, electroencephalography (EEG) was used to measure the time course of neural responses to proposed allocation schemes with different intentions. Twenty-eight participants played an ultimatum game as responders. They had to choose between accepting and rejecting the fair or unfair money allocation schemes of propo…
Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals
2019
Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. T…