Showing papers by "Stefan Haufe published in 2010"
TL;DR: Examples of novel BCI applications which provide evidence for the promising potential of BCI technology for non-medical uses are presented and distinct methodological improvements required to bring non- medical applications ofBCI technology to a diversity of layperson target groups are discussed.
Abstract: 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.
353 citations
TL;DR: This work proposes a novel technique, called sparsely connected sources analysis (SCSA), that can overcome the problem of volume conduction by modeling neural data innovatively with the following ingredients: the EEG/MEG is assumed to be a linear mixture of correlated sources following a multivariate autoregressive (MVAR) model.
Abstract: We propose a novel technique to assess functional brain connectivity in electroencephalographic (EEG)/magnetoencephalographic (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: 1) the EEG/MEG is assumed to be a linear mixture of correlated sources following a multivariate autoregressive (MVAR) model; 2) the demixing is estimated jointly with the source MVAR parameters; and 3) overfitting is avoided by using the group lasso penalty. This approach allows us to extract the appropriate level of crosstalk 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 it to a number of existing algorithms with excellent results.
112 citations
TL;DR: A freely accessible database http://eeg.pl/epi, containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy, offers a unique possibility for evaluating different EEG inverse solutions.
Abstract: 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.
37 citations
Proceedings Article•
18 Feb 2010TL;DR: In this article, the authors propose to enforce sparsity 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.
Abstract: 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 l1,2-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.
35 citations
11 Nov 2010
TL;DR: The approach is based on localization of single-trial Fourier coefficients using sparse basis field expansions (S-FLEX) and 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.
Abstract: 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.
5 citations
01 Jan 2010
TL;DR: The method’s ability to reconstruct simulated sources of random shape is demonstrated and it is shown that the accuracy of the recovered sources can be increased, when interrelated eld patterns are co-localized.
Abstract: 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 elds. It is designed to recover multiple sources of possibly dierent 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 eld 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 dierent types of motor imagery using real data from Brain-Computer Interface (BCI) sessions. Our approach based on single-trial localization of complex Fourier coecients yields class-specic focal sources in the sensorimotor cortices.