Search results for: Stefan Haufe

Items from 1 to 20 out of 31 results

article

Assessment of the Electrochemical Synthesis Route to Silicones Starting from Silica as Feedstock

Alexander D. Beck, Lukas Schäffer, Stefan Haufe, Siegfried R. Waldvogel

European Journal of Organic Chemistry > 2022 > 47 > n/a - n/a

We successfully achieved methylation of various SiO₂ sources to cyclic methylsiloxanes via electroreduction reaction. Contrary to previous assumptions, the reaction does not start from methanol as methyl radical source, that results in methoxylation of the electrolyte solvent. Methylammonium cations were found to enable the direct conversion, strongly dependent on the radical intermediate stabilization...

article

Quantifying the Effect of Demixing Approaches on Directed Connectivity Estimated Between Reconstructed EEG Sources

Alessandra Anzolin, Paolo Presti, Frederik Steen, Laura Astolfi, more

Brain Topography > 2019 > 32 > 4 > 655-674

Electrical activity recorded on the scalp using electroencephalography (EEG) results from the mixing of signals originating from different regions of the brain as well as from artifactual sources. In order to investigate the role of distinct brain areas in a given experiment, the signal recorded on the sensors is typically projected back into the brain (source reconstruction) using algorithms that...

article

Elucidating relations between fMRI, ECoG, and EEG through a common natural stimulus

Stefan Haufe, Paul DeGuzman, Simon Henin, Michael Arcaro, more

NeuroImage > 2018 > 179 > C > 79-91

Human brain mapping relies heavily on fMRI, ECoG and EEG, which capture different physiological signals. Relationships between these signals have been established in the context of specific tasks or during resting state, often using spatially confined concurrent recordings in animals. But it is not certain whether these correlations generalize to other contexts relevant for human cognitive neuroscience...

article

Consistency of EEG source localization and connectivity estimates

Keyvan Mahjoory, Vadim V. Nikulin, Loïc Botrel, Klaus Linkenkaer-Hansen, more

NeuroImage > 2017 > 152 > C > 590-601

As the EEG inverse problem does not have a unique solution, the sources reconstructed from EEG and their connectivity properties depend on forward and inverse modeling parameters such as the choice of an anatomical template and electrical model, prior assumptions on the sources, and further implementational details. In order to use source connectivity analysis as a reliable research tool, there is...

article

The New York Head—A precise standardized volume conductor model for EEG source localization and tES targeting

Yu Huang, Lucas C. Parra, Stefan Haufe

NeuroImage > 2016 > 140 > C > 150-162

In source localization of electroencephalograpic (EEG) signals, as well as in targeted transcranial electric current stimulation (tES), a volume conductor model is required to describe the flow of electric currents in the head. Boundary element models (BEM) can be readily computed to represent major tissue compartments, but cannot encode detailed anatomical information within compartments. Finite...

article

A Simulation Framework for Benchmarking EEG-Based Brain Connectivity Estimation Methodologies

Stefan Haufe, Arne Ewald

Brain Topography > 2019 > 32 > 4 > 625-642

Due to its high temporal resolution, electroencephalography (EEG) is widely used to study functional and effective brain connectivity. Yet, there is currently a mismatch between the vastness of studies conducted and the degree to which the employed analyses are theoretically understood and empirically validated. We here provide a simulation framework that enables researchers to test their analysis...

article

Validity of Time Reversal for <?Pub _newline ?>Testing Granger Causality

Irene Winkler, Danny Panknin, Daniel Bartz, Klaus-Robert Muller, more

IEEE Transactions on Signal Processing > 2016 > 64 > 11 > 2746 - 2760

Inferring causal interactions from observed data is a challenging problem, especially in the presence of measurement noise. To alleviate the problem of spurious causality, Haufe <etal/> (2013) proposed to contrast measures of information flow obtained on the original data against the same measures obtained on time-reversed data. They show that this procedure, time-reversed Granger causality...

article

Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data

Sven Dahne, Felix Bieszmann, Wojciech Samek, Stefan Haufe, more

Proceedings of the IEEE > 2015 > 103 > 9 > 1507 - 1530

Multimodal data are ubiquitous in engineering, communications, robotics, computer vision, or more generally speaking in industry and the sciences. All disciplines have developed their respective sets of analytic tools to fuse the information that is available in all measured modalities. In this paper, we provide a review of classical as well as recent machine learning methods (specifically factor...

article

Solving the EEG inverse problem based on space–time–frequency structured sparsity constraints

Sebastián Castaño-Candamil, Johannes Höhne, Juan-David Martínez-Vargas, Xing-Wei An, more

NeuroImage > 2015 > 118 > C > 598-612

We introduce STOUT (spatio-temporal unifying tomography), a novel method for the source analysis of electroencephalograpic (EEG) recordings, which is based on a physiologically-motivated source representation. Our method assumes that only a small number of brain sources are active throughout a measurement, where each of the sources exhibits focal (smooth but localized) characteristics in space, time...

chapter

An extendable simulation framework for benchmarking EEG-based brain connectivity estimation methodologies

Stefan Haufe

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) > 7562 - 7565

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Due to its high temporal resolution, electroencephalography (EEG) is a promising research tool for studying functional and effective brain interaction. Yet, it is rather uncommon for researchers to validate their connectivity estimation methodologies prior to applying them to real data, even though problems have been pointed out regarding the validity of some of the predominant approaches. We here...

article

Identifying Granger causal relationships between neural power dynamics and variables of interest

Irene Winkler, Stefan Haufe, Anne K. Porbadnigk, Klaus-Robert Müller, more

NeuroImage > 2015 > 111 > C > 489-504

Power modulations of oscillations in electro- and magnetoencephalographic (EEG/MEG) signals have been linked to a wide range of brain functions. To date, most of the evidence is obtained by correlating bandpower fluctuations to specific target variables such as reaction times or task ratings, while the causal links between oscillatory activity and behavior remain less clear. Here, we propose to identify...

article

Dimensionality reduction for the analysis of brain oscillations

Stefan Haufe, Sven Dähne, Vadim V. Nikulin, S. Haufe, more

NeuroImage > 2014 > 101 > Complete > 583-597

Neuronal oscillations have been shown to be associated with perceptual, motor and cognitive brain operations. While complex spatio-temporal dynamics are a hallmark of neuronal oscillations, they also represent a formidable challenge for the proper extraction and quantification of oscillatory activity with non-invasive recording techniques such as EEG and MEG. In order to facilitate the study of neuronal...

article

The effect of linear mixing in the EEG on Hurst exponent estimation

Duncan A.J. Blythe, Stefan Haufe, Klaus-Robert Müller, Vadim V. Nikulin

NeuroImage > 2014 > 99 > C > 377-387

Although the long-range temporal correlation (LRTC) of the amplitude fluctuations of neuronal EEG/MEG oscillations is widely acknowledged, the majority of studies to date have been performed in sensor space, disregarding the mixing effects implied by volume conduction and confounding noise. While the effect of mixing on the evaluation of evoked responses and connectivity measures has been extensively...

article

Finding brain oscillations with power dependencies in neuroimaging data

Sven Dähne, Vadim V. Nikulin, David Ramírez, Peter J. Schreier, more

NeuroImage > 2014 > 96 > C > 334-348

Phase synchronization among neuronal oscillations within the same frequency band has been hypothesized to be a major mechanism for communication between different brain areas. On the other hand, cross-frequency communications are more flexible allowing interactions between oscillations with different frequencies. Among such cross-frequency interactions amplitude-to-amplitude interactions are of a...

chapter

Parameter interpretation, regularization and source localization in multivariate linear models

Stefan Haufe, Frank Meinecke, Kai Gorgen, Sven Dahne, more

2014 International Workshop on Pattern Recognition in Neuroimaging > 1 - 4

2014 International Workshop on Pattern Recognition in Neuroimaging (PRNI)

Neuroimaging data are frequently analyzed with multivariate methods. Models expressing the data as a function of underlying factors related to the brain processes under study (signals) are called forward models, while models reversing this functional relationship are called backward models. Weigth vectors of backward models (called extraction filters) indicate the measurement channels informative...

article

On the interpretation of weight vectors of linear models in multivariate neuroimaging

Stefan Haufe, Frank Meinecke, Kai Görgen, Sven Dähne, more

NeuroImage > 2014 > 87 > C > 96-110

The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental...

chapter

Brain-computer interface for smart vehicle: Detection of braking intention during simulated driving

Jeong-Woo Kim, Il-Hwa Kim, Stefan Haufe, Seong-Whan Lee

2014 International Winter Workshop on Brain-Computer Interface (BCI) > 1 - 3

2014 International Winter Workshop on Brain-Computer Interface (BCI)

Neuro-driving simulation framework was proposed in this article for studying neural correlates of braking intention in diversified driving situations. In addition, the possibility was investigated that these neural correlates can be used to detect a participant's braking intention prior to the behavioral response. Electroencephalographic (EEG) and electromyographic (EMG) signals were measured from...

article

SPoC: A novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters

Sven Dähne, Frank C. Meinecke, Stefan Haufe, Johannes Höhne, more

NeuroImage > 2014 > 86 > Complete > 111-122

Previously, modulations in power of neuronal oscillations have been functionally linked to sensory, motor and cognitive operations. Such links are commonly established by relating the power modulations to specific target variables such as reaction times or task ratings. Consequently, the resulting spatio-spectral representation is subjected to neurophysiological interpretation. As an alternative,...

chapter

Detection of multi-class emergency situations during simulated driving from ERP

Il-Hwa Kim, Jeong-Woo Kim, Stefan Haufe, Seong-Whan Lee

2013 International Winter Workshop on Brain-Computer Interface (BCI) > 49 - 51

2013 International Winter Workshop on Brain-Computer Interface (BCI)

We present a driving simulator study investigating whether a driver's braking intention in emergency situations can be detected under more general circumstances than previously described in the literature. Precisely, we here simulated three kinds of realistic emergency situations instead of only one as considered in Haufe et al., 2011. For each of the three situations, the analysis of electroencephalography...

chapter

Decoding cognitive brain states

Anne K. Porbadnigk, Matthias S. Treder, Siamac Fazli, Michael Tangermann, more

2013 International Winter Workshop on Brain-Computer Interface (BCI) > 16 - 18

2013 International Winter Workshop on Brain-Computer Interface (BCI)

The last years have seen a rise in interest in using BCI methodology for investigating non-medical questions beyond the purpose of communication and control. This abstract first provides a short introduction to BCI challenges from a machine learning perspective. The remaining sections present selected applications of BCI discussing in particular the use of EEG in combination with BCI methods for investigating...

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INFONA - science communication portal

Search results for: Stefan Haufe

Assessment of the Electrochemical Synthesis Route to Silicones Starting from Silica as Feedstock

Quantifying the Effect of Demixing Approaches on Directed Connectivity Estimated Between Reconstructed EEG Sources

Elucidating relations between fMRI, ECoG, and EEG through a common natural stimulus

Consistency of EEG source localization and connectivity estimates

The New York Head—A precise standardized volume conductor model for EEG source localization and tES targeting

A Simulation Framework for Benchmarking EEG-Based Brain Connectivity Estimation Methodologies

Validity of Time Reversal for <?Pub _newline ?>Testing Granger Causality

Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data

Solving the EEG inverse problem based on space–time–frequency structured sparsity constraints

An extendable simulation framework for benchmarking EEG-based brain connectivity estimation methodologies

Identifying Granger causal relationships between neural power dynamics and variables of interest

Dimensionality reduction for the analysis of brain oscillations

The effect of linear mixing in the EEG on Hurst exponent estimation

Finding brain oscillations with power dependencies in neuroimaging data

Parameter interpretation, regularization and source localization in multivariate linear models

On the interpretation of weight vectors of linear models in multivariate neuroimaging

Brain-computer interface for smart vehicle: Detection of braking intention during simulated driving

SPoC: A novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters

Detection of multi-class emergency situations during simulated driving from ERP

Decoding cognitive brain states

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