Transfer Learning for P300 Brain-Computer Interfaces by Joint Alignment of Feature Vectors.

This article presents a new transfer learning method named group learning, that jointly aligns multiple domains (many-to-many) and an extension named fast alignment that aligns any further domain to previously aligned group of domains (many-to-one). The proposed group alignment algorithm (GALIA) is evaluated on brain-computer interface (BCI) data and optimal hyper-parameter values of the algorithm are studied for classification performance and computational cost. Six publicly available P300 databases comprising 333 sessions from 177 subjects are used.

Dynamic Modeling of Carbon Dioxide Transport through the Skin Using a Capnometry Wristband.

The development of a capnometry wristband is of great interest for monitoring patients at home. We consider a new architecture in which a non-dispersive infrared (NDIR) optical measurement is located close to the skin surface and is combined with an open chamber principle with a continuous circulation of air flow in the collection cell. We propose a model for the temporal dynamics of the carbon dioxide exchange between the blood and the gas channel inside the device.

Micro-pursuit: A class of fixational eye movements correlating with smooth, predictable, small-scale target trajectories.

Humans generate ocular pursuit movements when a moving target is tracked throughout the visual field. In this article, we show that pursuit can be generated and measured at small amplitudes, at the scale of fixational eye movements, and tag these eye movements as micro-pursuits. During micro-pursuits, gaze direction correlates with a target's smooth, predictable target trajectory.

Graph Lasso-Based Test for Evaluating Functional Brain Connectivity in Sickle Cell Disease.

Sickle cell disease (SCD) is a vascular disorder that is often associated with recurrent ischemia-reperfusion injury, anemia, vasculopathy, and strokes. These cerebral injuries are associated with neurological dysfunction, limiting the full developing potential of the patient. However, recent large studies of SCD have demonstrated that cognitive impairment occurs even in the absence of brain abnormalities on conventional magnetic resonance imaging (MRI).

Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability.

Spectral unmixing is one of the main research topics in hyperspectral imaging. It can be formulated as a source separation problem, whose goal is to recover the spectral signatures of the materials present in the observed scene (called endmembers) as well as their relative proportions (called fractional abundances), and this for every pixel in the image. A linear mixture model (LMM) is often used for its simplicity and ease of use, but it implicitly assumes that a single spectrum can be completely representative of a material.

FUNCTIONAL CONNECTIVITY ANALYSIS FOR THALASSEMIA DISEASE BASED ON A GRAPHICAL LASSO MODEL.

Thalassemia is a congenital disorder of hemoglobin synthesis which can lead to thromboembolic events and stroke in the brain. In this work we propose to use a functional connectivity model to discriminate between control and diseased subjects. Our connectivity measure is based on functional magnetic resonance imaging, and hence common variations of the blood oxygenation level in spatially distant areas.

Independent Vector Analysis for Gradient Artifact Removal in Concurrent EEG-fMRI Data.

We consider the problem of removing gradient artifact from electroencephalogram (EEG) signal, recorded concurrently with functional magnetic resonance imaging (fMRI) acquisition. We estimate the artifact by exploiting its quasi-periodicity over the epochs and its similarity over the different channels by using independent vector analysis, a recent extension of independent component analysis for multiple datasets. The method fully makes use of the spatio-temporal information by using spatial dependences across channels to estimate the artifact for a particular channel.

Deriving a multi-subject functional-connectivity atlas to inform connectome estimation.

The estimation of functional connectivity structure from functional neuroimaging data is an important step toward understanding the mechanisms of various brain diseases and building relevant biomarkers. Yet, such inferences have to deal with the low signal-to-noise ratio and the paucity of the data. With at our disposal a steadily growing olume of publicly available neuroimaging data, it is however possible to improve the estimation procedures involved in connectome mapping.

Impact of autocorrelation on functional connectivity.

Although the impact of serial correlation (autocorrelation) in residuals of general linear models for fMRI time-series has been studied extensively, the effect of autocorrelation on functional connectivity studies has been largely neglected until recently. Some recent studies based on results from economics have questioned the conventional estimation of functional connectivity and argue that not correcting for autocorrelation in fMRI time-series results in "spurious" correlation coefficients. In this paper, first we assess the effect of autocorrelation on Pearson correlation coefficient through theoretical approximation and simulation.

Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis.

Recent work on both task-induced and resting-state functional magnetic resonance imaging (fMRI) data suggests that functional connectivity may fluctuate, rather than being stationary during an entire scan. Most dynamic studies are based on second-order statistics between fMRI time series or time courses derived from blind source separation, e.g.

Independent component analysis for brain FMRI does indeed select for maximal independence.

A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. The argument was supported by a series of experiments on synthetic data.

A non-orthogonal SVD-based decomposition for phase invariant error-related potential estimation.

The estimation of the Error Related Potential from a set of trials is a challenging problem. Indeed, the Error Related Potential is of low amplitude compared to the ongoing electroencephalographic activity. In addition, simple summing over the different trials is prone to errors, since the waveform does not appear at an exact latency with respect to the trigger.

An extension of the Canonical Correlation Analysis to the case of multiple observations of two groups of variables.

In this contribution we present a method that extends the Canonical Correlation Analysis for two groups of variables to the case of multiple conditions. Contrary to the extensions in literature based on augmenting the number of variable groups, the addition of conditions allows for a more robust estimate of the canonical correlation structure inherently present in the data. Algorithms to solve the estimation problem are based on joint approximate diagonalization algorithms for matrix sets.

EEG sensor selection by sparse spatial filtering in P300 speller brain-computer interface.

A Brain-Computer Interface (BCI) is a specific type of human-machine interface that enables communication between a subject/patient and a computer by direct control from decoding of brain activity. This paper deals with the P300-speller application that enables to write a text based on the oddball paradigm. To improve the ergonomics and minimize the cost of such a BCI, reducing the number of electrodes is mandatory.

A contrast function for independent component analysis without permutation ambiguity.

This brief deals with the problem of blind source separation (BSS) via independent component analysis (ICA). We prove that a linear combination of the separator output fourth-order marginal cumulants (kurtoses) is a valid contrast function for ICA under prewhitening if the weights have the same sign as the source kurtoses. If, in addition, the source kurtoses are different and so are the linear combination weights, the contrast eliminates the permutation ambiguity typical to ICA, as the estimated sources are sorted at the separator output according to their kurtosis values in the same order as the weights.

Atrial activity estimation from atrial fibrillation ECGs by blind source extraction based on a conditional maximum likelihood approach.

This work presents a spatial filtering method for the estimation of atrial fibrillation activity in the cutaneous electrocardiogram. A linear extraction filter is obtained by maximising the extractor output power on the significant spectral support of the signal of interest. An iterative procedure based on a quasi-maximum likelihood estimator is proposed to jointly estimate the significant spectral support and the extraction filter.

Cumulant matching for independent source extraction.

In this work we show how one can make use of priors on signal statistics under the form of cumulant guesses to extract an independent source from an observed mixture. The advantage of using statistical priors on the signal lies in the fact that no specific knowledge is needed about its temporal behavior, neither about its spatial distribution. We show that these statistics can be obtained either by reasoning on the theoretical values of a supposed waveform, either by using a subset of the observations from which we know that their statistics are merely hindered by interferences.

The exploitation of spatial topographies for atrial signal extraction in atrial fibrillation ECGs.

The accuracy in the extraction of the atrial activity (AA) from electrocardiogram (ECG) signals recorded during atrial fibrillation (AF) episodes plays an important role in the analysis and characterization of atrial arrhythmias. The present contribution puts forward a method for AA signal extraction based on a blind source separation (BSS) formulation. The latter exploits spatial information on the different components in the ECG related or not to AF.

Eigenvector analysis for separation of a spectrally concentrated source from a mixture.

An objective function is presented to recover a spectrally narrow band signal from multichannel measurements, as in electrocardiogram recordings of atrial fibrillation. The criterion can be efficiently maximized through the eigenvalue decomposition of some spectral correlation matrices of the whitened observations across appropriately chosen frequency bands. It is conjectured that the global optimum so attained recovers the source of interest when its spectral concentration around its modal frequency is maximal.

Removing ocular movement artefacts by a joint smoothened subspace estimator.

To cope with the severe masking of background cerebral activity in the electroencephalogram (EEG) by ocular movement artefacts, we present a method which combines lower-order, short-term and higher-order, long-term statistics. The joint smoothened subspace estimator (JSSE) calculates the joint information in both statistical models, subject to the constraint that the resulting estimated source should be sufficiently smooth in the time domain (i.e.

Extraction of the atrial activity from the ECG based on independent component analysis with prior knowledge of the source kurtosis signs.

In this work it will be shown that a contrast for independent component analysis based on prior knowledge of the source kurtosis signs (ica-sks) is able to extract atrial activity from the electrocardiogram when a constrained updating is introduced. A spectral concentration measure is used, only allowing signal pair updates when spectral concentration augments. This strategy proves to be valid for independent source extraction with priors on the spectral concentration.

Extracting the spike process from the EEG by spatially constrained ICA.

Epileptic patients often show interictal epileptic discharges (IED's) in the electroencephalogram (EEG) recorded between seizures. This epileptiform activity is in many cases related to the location of the seizure onset, and is believed to reflect the frequency of the seizures. We present a fully automated technique that is able to extract the IED's from the EEG, despite the obscuring artifacts.

Muscle and eye movement artifact removal prior to EEG source localization.

Muscle and eye movement artifacts are very prominent in the ictal EEG of patients suffering from epilepsy, thus making the dipole localization of ictal activity very unreliable. Recently, two techniques (BSS-CCA and pSVD) were developed to remove those artifacts. The purpose of this study is to assess whether the removal of muscle and eye movement artifacts improves the EEG dipole source localization.