Stefan Haufe

Publications

• Alleviating the Influence of Weak Data Asymmetries on Granger-Causal Analyses.

  Stefan Haufe, Vadim V. Nikulin, Guido Nolte

  Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Tel Aviv, Israel, March 12-15, 2012. Proceedings; 01/2012
• 2.34

  Impact points

  Automatic classification of artifactual ICA-components for artifact removal in EEG signals.

  Irene Winkler, Stefan Haufe, Michael Tangermann

  Behavioral and brain functions : BBF. 08/2011; 7:30.

  Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component... [more] Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.
• 3.74

  Impact points

  EEG potentials predict upcoming emergency brakings during simulated driving.

  Stefan Haufe, Matthias S Treder, Manfred F Gugler, Max Sagebaum, Gabriel Curio, Benjamin Blankertz

  Journal of neural engineering. 07/2011; 8(5):056001.

  Emergency braking assistance has the potential to prevent a large number of car crashes. State-of-the-art systems operate in two stages. Basic safety measures are adopted once external sensors indicate a potential upcoming crash. If further activity at the brake pedal is detected, the system automat... [more] Emergency braking assistance has the potential to prevent a large number of car crashes. State-of-the-art systems operate in two stages. Basic safety measures are adopted once external sensors indicate a potential upcoming crash. If further activity at the brake pedal is detected, the system automatically performs emergency braking. Here, we present the results of a driving simulator study indicating that the driver's intention to perform emergency braking can be detected based on muscle activation and cerebral activity prior to the behavioural response. Identical levels of predictive accuracy were attained using electroencephalography (EEG), which worked more quickly than electromyography (EMG), and using EMG, which worked more quickly than pedal dynamics. A simulated assistance system using EEG and EMG was found to detect emergency brakings 130 ms earlier than a system relying only on pedal responses. At 100 km h(-1) driving speed, this amounts to reducing the braking distance by 3.66 m. This result motivates a neuroergonomic approach to driving assistance. Our EEG analysis yielded a characteristic event-related potential signature that comprised components related to the sensory registration of a critical traffic situation, mental evaluation of the sensory percept and motor preparation. While all these components should occur often during normal driving, we conjecture that it is their characteristic spatio-temporal superposition in emergency braking situations that leads to the considerable prediction performance we observed.
• 5.74

  Impact points

  Single-trial analysis and classification of ERP components--a tutorial.

  Benjamin Blankertz, Steven Lemm, Matthias Treder, Stefan Haufe, Klaus-Robert Müller

  NeuroImage. 05/2011; 56(2):814-25.

  Analyzing brain states that correspond to event related potentials (ERPs) on a single trial basis is a hard problem due to the high trial-to-trial variability and the unfavorable ratio between signal (ERP) and noise (artifacts and neural background activity). In this tutorial, we provide a comprehen... [more] Analyzing brain states that correspond to event related potentials (ERPs) on a single trial basis is a hard problem due to the high trial-to-trial variability and the unfavorable ratio between signal (ERP) and noise (artifacts and neural background activity). In this tutorial, we provide a comprehensive framework for decoding ERPs, elaborating on linear concepts, namely spatio-temporal patterns and filters as well as linear ERP classification. However, the bottleneck of these techniques is that they require an accurate covariance matrix estimation in high dimensional sensor spaces which is a highly intricate problem. As a remedy, we propose to use shrinkage estimators and show that appropriate regularization of linear discriminant analysis (LDA) by shrinkage yields excellent results for single-trial ERP classification that are far superior to classical LDA classification. Furthermore, we give practical hints on the interpretation of what classifiers learned from the data and demonstrate in particular that the trade-off between goodness-of-fit and model complexity in regularized LDA relates to a morphing between a difference pattern of ERPs and a spatial filter which cancels non task-related brain activity.
• 5.74

  Impact points

  Large-scale EEG/MEG source localization with spatial flexibility.

  Stefan Haufe, Ryota Tomioka, Thorsten Dickhaus, Claudia Sannelli, Benjamin Blankertz, Guido Nolte, Klaus-Robert Müller

  NeuroImage. 01/2011; 54(2):851-9.

  We propose a novel approach to solving the electro-/magnetoencephalographic (EEG/MEG) inverse problem which is based upon a decomposition of the current density into a small number of spatial basis fields. It is designed to recover multiple sources of possibly different extent and depth, while being... [more] We propose a novel approach to solving the electro-/magnetoencephalographic (EEG/MEG) inverse problem which is based upon a decomposition of the current density into a small number of spatial basis fields. It is designed to recover multiple sources of possibly different extent and depth, while being invariant with respect to phase angles and rotations of the coordinate system. We demonstrate the method's ability to reconstruct simulated sources of random shape and show that the accuracy of the recovered sources can be increased, when interrelated field patterns are co-localized. Technically, this leads to large-scale mathematical problems, which are solved using recent advances in convex optimization. We apply our method for localizing brain areas involved in different types of motor imagery using real data from Brain-Computer Interface (BCI) sessions. Our approach based on single-trial localization of complex Fourier coefficients yields class-specific focal sources in the sensorimotor cortices.
• 3.05

  Impact points

  Open database of epileptic EEG with MRI and postoperational assessment of foci--a real world verification for the EEG inverse solutions.

  Piotr Zwoliński, Marcin Roszkowski, Jaroslaw Zygierewicz, Stefan Haufe, Guido Nolte, Piotr J Durka

  Neuroinformatics. 12/2010; 8(4):285-99.

  This paper introduces a freely accessible database http://eeg.pl/epi , containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical elec... [more] This paper introduces a freely accessible database http://eeg.pl/epi , containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical electroencephalography (EEG) recording (10-20 system) with inter-ictal discharges marked separately by an expert, (2) a full set of magnetic resonance imaging (MRI) scans for calculations of the realistic forward models, (3) structural placement of the epileptogenic zone, recognized by electrocorticography (ECoG) and post-surgical results, plotted on pre-surgical MRI scans in transverse, sagittal and coronal projections, (4) brief clinical description of each case. The main goal of this project is evaluation of possible improvements of localization of epileptic foci from the surface EEG recordings. These datasets offer a unique possibility for evaluating different EEG inverse solutions. We present preliminary results from a subset of these cases, including comparison of different schemes for the EEG inverse solution and preprocessing. We report also a finding which relates to the selective parametrization of single waveforms by multivariate matching pursuit, which is used in the preprocessing for the inverse solutions. It seems to offer a possibility of tracing the spatial evolution of seizures in time.

• Localization of class-related mu-rhythm desynchronization in motor imagery based brain-computer interface sessions.

  Stefan Haufe, Ryota Tomioka, Thorsten Dickhaus, Claudia Sannelli, Benjamin Blankertz, Guido Nolte, Klaus-Robert Muller

  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 01/2010; 2010:5137-40.

  We localize the sources of class-dependent event-related desynchronisation (ERD) of the mu-rhythm related to different types of motor imagery in Brain-Computer Interfacing (BCI) sessions. Our approach is based on localization of single-trial Fourier coefficients using sparse basis field expansions (... [more] We localize the sources of class-dependent event-related desynchronisation (ERD) of the mu-rhythm related to different types of motor imagery in Brain-Computer Interfacing (BCI) sessions. Our approach is based on localization of single-trial Fourier coefficients using sparse basis field expansions (S-FLEX). The analysis reveals focal sources in the sensorimotor cortices, a finding which can be regarded as a proof for the expected neurophysiological origin of the BCI control signal. As a technical contribution, we extend S-FLEX to the multiple measurement case in a way that the activity of different frequency bins within the mu-band is coherently localized.

• The Berlin Brain-Computer Interface: Non-Medical Uses of BCI Technology.

  Benjamin Blankertz, Michael Tangermann, Carmen Vidaurre, Siamac Fazli, Claudia Sannelli, Stefan Haufe, Cecilia Maeder, Lenny Ramsey, Irene Sturm, Gabriel Curio, Klaus-Robert Müller

  Frontiers in neuroscience. 01/2010; 4:198.

  Brain-computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and... [more] Brain-computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies.

• Modeling sparse connectivity between underlying brain sources for EEG/MEG

  Stefan Haufe, Ryota Tomioka, Guido Nolte, Klaus-Robert Mueller, Motoaki Kawanabe

  12/2009;

  We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the following ingredients: (a) the EEG is assumed to be a li... [more] We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the following ingredients: (a) the EEG is assumed to be a linear mixture of correlated sources following a multivariate autoregressive (MVAR) model, (b) the demixing is estimated jointly with the source MVAR parameters, (c) overfitting is avoided by using the Group Lasso penalty. This approach allows to extract the appropriate level cross-talk between the extracted sources and in this manner we obtain a sparse data-driven model of functional connectivity. We demonstrate the usefulness of SCSA with simulated data, and compare to a number of existing algorithms with excellent results. Comment: 9 pages, 6 figures

• Sparse Causal Discovery in Multivariate Time Series

  Stefan Haufe, Guido Nolte, Klaus-Robert Mueller, Nicole Kraemer

  01/2009;

  Our goal is to estimate causal interactions in multivariate time series. Using vector autoregressive (VAR) models, these can be defined based on non-vanishing coefficients belonging to respective time-lagged instances. As in most cases a parsimonious causality structure is assumed, a promising appro... [more] Our goal is to estimate causal interactions in multivariate time series. Using vector autoregressive (VAR) models, these can be defined based on non-vanishing coefficients belonging to respective time-lagged instances. As in most cases a parsimonious causality structure is assumed, a promising approach to causal discovery consists in fitting VAR models with an additional sparsity-promoting regularization. Along this line we here propose that sparsity should be enforced for the subgroups of coefficients that belong to each pair of time series, as the absence of a causal relation requires the coefficients for all time-lags to become jointly zero. Such behavior can be achieved by means of l1-l2-norm regularized regression, for which an efficient active set solver has been proposed recently. Our method is shown to outperform standard methods in recovering simulated causality graphs. The results are on par with a second novel approach which uses multiple statistical testing. Comment: to appear in Journal of Machine Learning Research, Proceedings of the NIPS'08 workshop on Causality

• Practicing fast-decision BCI using a "goalkeeper" paradigm

  Lenny Ramsey, Michael Tangermann, Stefan Haufe, Benjamin Blankertz

  BMC Neuroscience. 01/2009;
• 5.38

  Impact points

  Now You'll Feel It-Now You Won't: EEG Rhythms Predict the Effectiveness of Perceptual Masking.

  Ruth Schubert, Stefan Haufe, Felix Blankenburg, Arno Villringer, Gabriel Curio

  Journal of cognitive neuroscience. 01/2009;

  Abstract Do ongoing brain states determine conscious perception of an upcoming stimulus? Using the high temporal resolution of EEG, we investigated the relationship between prestimulus neuronal oscillations and the perceptibility of two competing somatosensory stimuli embedded in a backward masking ... [more] Abstract Do ongoing brain states determine conscious perception of an upcoming stimulus? Using the high temporal resolution of EEG, we investigated the relationship between prestimulus neuronal oscillations and the perceptibility of two competing somatosensory stimuli embedded in a backward masking paradigm. We identified two prestimulus EEG signatures predictive for a suprathreshold yet weak target stimulus to become perceptually resistant against masking by a stronger distractor stimulus: (i) over left frontal cortex a desynchronization of the regional beta rhythm ( approximately 20 Hz) 500 msec prior to a perceived target, and (ii) a subsequent additional attenuation of both mu ( approximately 10 Hz) and beta "idling" rhythms is following over those pericentral sensorimotor cortices which are going to process the upcoming target stimulus. Furthermore, across subjects the probability for target perception strongly correlates with the individual absolute level of pretarget amplitudes in these frequency bands and locations. These signatures significantly differed from the EEG characteristics preceding detected and undetected single stimuli. We suggest that the early activation of left frontal areas involved in top-down attentional control is critical for preventing backward masking and leads the preparation of primary sensory cortices: The ensuing prestimulus suppression of sensory idling rhythms warrants an intensified poststimulus processing, and thus, effectively promotes conscious perception of suprathreshold target stimuli embedded into an ecologically relevant condition featuring competing environmental stimuli.
• 5.74

  Impact points

  Combining sparsity and rotational invariance in EEG/MEG source reconstruction.

  Stefan Haufe, Vadim V Nikulin, Andreas Ziehe, Klaus-Robert Müller, Guido Nolte

  NeuroImage. 06/2008;

  We introduce Focal Vector Field Reconstruction (FVR), a novel technique for the inverse imaging of vector fields. The method was designed to simultaneously achieve two goals: a) invariance with respect to the orientation of the coordinate system, and b) a preference for sparsity of the solutions and... [more] We introduce Focal Vector Field Reconstruction (FVR), a novel technique for the inverse imaging of vector fields. The method was designed to simultaneously achieve two goals: a) invariance with respect to the orientation of the coordinate system, and b) a preference for sparsity of the solutions and their spatial derivatives. This was achieved by defining the regulating penalty function, which renders the solutions unique, as a global l(1)-norm of local l(2)-norms. We show that the method can be successfully used for solving the EEG inverse problem. In the joint localization of 2-3 simulated dipoles, FVR always reliably recovers the true sources. The competing methods have limitations in distinguishing close sources because their estimates are either too smooth (LORETA, Minimum l(1)-norm) or too scattered (Minimum l(2)-norm). In both noiseless and noisy simulations, FVR has the smallest localization error according to the Earth Mover's Distance (EMD), which is introduced here as a meaningful measure to compare arbitrary source distributions. We also apply the method to the simultaneous localization of left and right somatosensory N20 generators from real EEG recordings. Compared to its peers FVR was the only method that delivered correct location of the source in the somatosensory area of each hemisphere in accordance with neurophysiological prior knowledge.

• Now You'll Feel It, Now You Won't: EEG Rhythms Predict the Effectiveness of Perceptual Masking

  Ruth Schubert, Stefan Haufe, Felix Blankenburg, Arno Villringer, Gabriel Curio

  Journal of cognitive neuroscience. 21(12):2407-2419.

  Do ongoing brain states determine conscious perception of an upcoming stimulus? Using the high temporal resolution of EEG, we investigated the relationship between prestimulus neuronal oscillations and the perceptibility of two competing somatosensory stimuli embedded in a backward masking paradigm.... [more] Do ongoing brain states determine conscious perception of an upcoming stimulus? Using the high temporal resolution of EEG, we investigated the relationship between prestimulus neuronal oscillations and the perceptibility of two competing somatosensory stimuli embedded in a backward masking paradigm. We identified two prestimulus EEG signatures predictive for a suprathreshold yet weak target stimulus to become perceptually resistant against masking by a stronger distractor stimulus: (i) over left frontal cortex a desynchronization of the regional beta rhythm (similar to 20 Hz) 500 msec prior to a perceived target, and (ii) a subsequent additional attenuation of both mu (similar to 10 Hz) and beta "idling'' rhythms over those pericentral sensorimotor cortices which are going to process the upcoming target stimulus. Furthermore, across subjects the probability for target perception strongly correlates with the individual absolute level of pre-target amplitudes in these frequency bands and locations. These signatures significantly differed from the EEG characteristics preceding detected and undetected single stimuli. We suggest that the early activation of left frontal areas involved in top-down attentional control is critical for preventing backward masking and leads the preparation of primary sensory cortices: The ensuing prestimulus suppression of sensory idling rhythms warrants an intensified poststimulus processing, and thus, effectively promotes conscious perception of suprathreshold target stimuli embedded into an ecologically relevant condition featuring competing environmental stimuli.

• Combining sparsity and rotational invariance in EEG/MEG source reconstruction

  Stefan Haufe, Vadim V. Nikulin, Andreas Ziehe, Klaus-Robert Müller, Guido Nolte

  NeuroImage.

  We introduce Focal Vector Field Reconstruction (FVR), a novel technique for the inverse imaging of vector fields. The method was designed to simultaneously achieve two goals: a) invariance with respect to the orientation of the coordinate system, and b) a preference for sparsity of the solutions and... [more] We introduce Focal Vector Field Reconstruction (FVR), a novel technique for the inverse imaging of vector fields. The method was designed to simultaneously achieve two goals: a) invariance with respect to the orientation of the coordinate system, and b) a preference for sparsity of the solutions and their spatial derivatives. This was achieved by defining the regulating penalty function, which renders the solutions unique, as a global ℓ1-norm of local ℓ2-norms. We show that the method can be successfully used for solving the EEG inverse problem. In the joint localization of 2–3 simulated dipoles, FVR always reliably recovers the true sources. The competing methods have limitations in distinguishing close sources because their estimates are either too smooth (LORETA, Minimum ℓ1-norm) or too scattered (Minimum ℓ2-norm). In both noiseless and noisy simulations, FVR has the smallest localization error according to the Earth Mover's Distance (EMD), which is introduced here as a meaningful measure to compare arbitrary source distributions. We also apply the method to the simultaneous localization of left and right somatosensory N20 generators from real EEG recordings. Compared to its peers FVR was the only method that delivered correct location of the source in the somatosensory area of each hemisphere in accordance with neurophysiological prior knowledge.

• Open Database of Epileptic EEG with MRI and Postoperational Assessment of Foci—a Real World Verification for the EEG Inverse Solutions

  Piotr Zwoliński, Marcin Roszkowski, Jaroslaw Żygierewicz, Stefan Haufe, Guido Nolte, Piotr J Durka

  This paper introduces a freely accessible database http://eeg.pl/epi, containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical elect... [more] This paper introduces a freely accessible database http://eeg.pl/epi, containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical electroencephalography (EEG) recording (10–20 system) with inter-ictal discharges marked separately by an expert, (2) a full set of magnetic resonance imaging (MRI) scans for calculations of the realistic forward models, (3) structural placement of the epileptogenic zone, recognized by electrocorticography (ECoG) and post-surgical results, plotted on pre-surgical MRI scans in transverse, sagittal and coronal projections, (4) brief clinical description of each case. The main goal of this project is evaluation of possible improvements of localization of epileptic foci from the surface EEG recordings. These datasets offer a unique possibility for evaluating different EEG inverse solutions. We present preliminary results from a subset of these cases, including comparison of different schemes for the EEG inverse solution and preprocessing. We report also a finding which relates to the selective parametrization of single waveforms by multivariate matching pursuit, which is used in the preprocessing for the inverse solutions. It seems to offer a possibility of tracing the spatial evolution of seizures in time.