Dania Gutiérrez

Center for Research and Advanced Studies of the National Polytechnic Institute | Cinvestav · Unidad Monterrey

Techniques based on electroencephalography (EEG) measure the electric potentials on the scalp and process them to infer the location, distribution, and intensity of underlying neural activity. Accuracy in estimating these parameters is highly sensitive to uncertainty in the conductivities of the head tissues. Furthermore, dissimilarities among indi...

We propose to assess the process of learning a task using electroencephalographic (EEG) measurements. In particular, we quantify changes in brain activity associated to the progression of the learning experience through the functional analysis-of-variances (FANOVA) estimators of the EEG power spectral density (PSD). Such functional estimators provi...

In this paper, we propose spatial filters for a linear regression model, which are based on the minimum-variance pseudo-unbiased reduced-rank estimation (MV-PURE) framework. As a sample application, we consider the problem of reconstruction of brain activity from electroencephalographic (EEG) or magnetoencephalographic (MEG) measurements. The propo...

New trends on brain-computer interface (BCI) design are aiming to combine this technology with immersive virtual reality in order to provide a sense of realism to its users. In this study, we propose an experimental BCI to control an immersive telepresence system using motor imagery (MI). The system is immersive in the sense that the users can cont...

In this paper, we propose a statistical selection procedure by which various mental tasks can be characterized by specific brain functional connectivity. Different connectivity patterns are identified by the partial directed coherence (PDC) which is a frequency-domain metric that provides information about directionality in the interaction between...

In this paper, we propose new neural activity indices for the solution of the inverse problem of localizing sources of cortical activity from electroencephalography (EEG) measurements. Such indices are based on reduced-rank beamformers, specifically the generalized sidelobe canceler (GSC), and with the purpose of suppressing the contribution of int...

We propose an experimental technique for the estimation of quality parameters of post-mortem human bone samples with signs of osteoporosis. Since we were interested in characterizing bone microstructure by evaluating porosity, trabecular thickness, and space, we obtained reference values of those parameters for some of our samples through micro-com...

We analyze the functional connectivity of the cortico-cardiac-muscular network during muscle fatigability due to exercise. For our experiments, we recruited ten volunteers who performed two cycling routines. Our volunteers were classified according to physical activity level as active or sedentary. First, we measured the electromyography (EMG) sign...

We propose a method based on the ensemble Kalman filter (EnKF) together with quantitative electroencephalogram (QEEG) coherence and power spectrum analysis for evaluating changes in brain activity associated with cognitive processes. Such analysis framework has been widely used in the context of data assimilation (DA) in areas such as geosciences,...

The use of transcranial direct current stimulation (tDCS) has been related to the improvement of motor and learning tasks. The current research studies the effects of an asymmetric tDCS setup over brain connectivity, when the subject is performing a motor imagery (MI) task during five consecutive days. A brain–computer interface (BCI) based on elec...

In this paper, we evaluate a semiautonomous brain-computer interface (BCI) for manipulation tasks. In such a system, the user controls a robotic arm through motor imagery commands. In traditional process-control BCI systems, the user has to provide those commands continuously in order to manipulate the effector of the robot step-by-step, which resu...

In this paper, we evaluate a semi-autonomous brain-computer interface (BCI) for manipulation tasks. In such system, the user controls a robotic arm through motor imagery commands. In traditional process-control BCI systems, the user has to provide those commands continuously in order manipulate the effector of the robot step-by-step, which results...

Lower-limb exoskeletons have been used in gait rehabilitation to facilitate the restoration of motor skills. These robotics systems could be complemented by Brain-Computer Interfaces (BCIs) to assist or rehabilitate people with walking disabilities. In this preliminary study, electroencephalography-based brain functional connectivity is analyzed du...

Transcranial direct current stimulation (tDCS) is a non-invasive technique for brain stimulation capable of modulating brain excitability. Although beneficial effects of tDCS have been shown, the underlying brain mechanisms have not been described. In the present study, we aim to investigate the effects of tDCS on EEG-based functional connectivity,...

We propose an artificial vision algorithm for a semi-autonomous brain-computer interface (BCI). The interface was designed in such a way that users are able to manipulate a robotic arm to pick up an object from a table and place it in one of two possible locations indicated as goal disks, and the manipulation is performed without any concern about...

We propose to use the partial directed coherence (PDC) to analyze the coupling between pairs of electroencephalographic (EEG) measurements during movement imagery tasks, as well as the directionality of such coupling. For this, we consider the multivariate autoregressive model of the signals from a selection of eleven EEG channels that are assumed...

We consider the problem of reconstruction of brain activity from electroencephalography (EEG) or magnetoencephalography (MEG) using spatial filtering (beamforming). We propose spatial filters which are based on the minimum-variance pseudo-unbiased reduced-rank estimation (MV-PURE) framework. They come in two flavours, depending whether the EEG/MEG...

We analyzed the conditions under which microcracks, generated by fatigue, affect the fracture properties of bones; this has clinical relevance to stress fractures and osteoporosis. A novel theoretical model was developed to describe microcrack behaviour, using probabilistic analysis and the concept of a characteristic length. In this way we identif...

Purpose: Although significant differences in bone properties have been extensively studied, results vary when bones are exported to gamma radiation of a range usually used for sterilization purposes (25-35 kGy). Hence, the aim of this work was the study of the mechanical properties and microdamage development of human bones used as allografts foll...

We present a solution to the electroencephalographic (EEG) forward problem of computing the scalp electric potentials for the case when the head’s geometry is modeled using a four-shell ellipsoidal geometry and the brain sources with an equivalent current dipole (ECD). The proposed solution includes terms up to the fourth-order ellipsoidal harmonic...

We present a solution to the electroencephalographs (EEG) forward problem of computing the scalp electric potentials for the case when the head's geometry is modeled using a four-shell ellipsoidal geometry and the brain sources with an equivalent current dipole (ECD). The proposed solution includes terms up to the fourth-order ellipsoidal harmonics...

We present a forward modeling solution in the form of an array response kernel for magnetoencephalography. We consider the case when the brain's anatomy is approximated by an ellipsoid and an equivalent current dipole model is used to approximate brain sources. The proposed solution includes the contributions up to the third-order ellipsoidal harmo...

We consider the problem of reconstruction of brain activity from electroencephalography (EEG) and magnetoencephalography (MEG) using spatial filtering (beamforming). We assume the presence of interfering sources, whose activity may be highly correlated with activity of sources to be reconstructed. Such situation causes the celebrated linearly const...

The increase in computer power of the last few decades has allowed the resurgence of the theory behind spatial filtering (a.k.a. beamforming) and its application to array signal processing. That is the case of magnetoencephalographic (MEG) data, which relies on dense arrays of detectors in order to measure the brain activity non-invasively. In part...

We study the relationship between electroencephalographic (EEG) coherence and accuracy in operating a brain-computer interface (BCI). In our case, the BCI is controlled through motor imagery. Hence, a number of volunteers were trained using different training paradigms: classical visual feedback, auditory stimulation, and functional electrical stim...

We explore the possibility of assessing the acquisition of a new skill through electroencephalographic (EEG) measurements. In particular, we propose an experiment to monitor the process of learning to type using the Colemak keyboard layout during a twelve-lessons training. As a first step, we are interested in identifying statistically significant...

We propose a feature extraction method for multi-class electroencephalographic (EEG) signals based on their pairwise coherences. The coherence provides a sense of the brain's connectivity, and it is relevant as different regions of the brain must communicate between each other for the integration of sensory information. In our case, the process of...

We propose a method to use electroencephalographic (EEG) coherences as features in a brain–computer interface (BCI). The coherence provides a sense of the brain's connectivity, and it is relevant as different regions of the brain must communicate between each other for the integration of sensory information. In our case, the process of feature sele...

Localization of sources of brain electrical activity from electroencephalographic and magnetoencephalographic recordings is an ill-posed inverse problem. Therefore, the best one can hope for is to derive a source localization method which is guaranteed to find sources belonging to the set of possible solutions to this problem. Recently, a few metho...

We consider the problem of electroencephalography (EEG) and magnetoencephalography (MEG) source localization using beamforming techniques. Specifically, we propose a reduced-rank extension of the recently derived multi-source activity index (MAI), which itself is an extension of the classical neural activity index to the multi-source case. We show...

We study the use of nonparametric multicompare statistical tests on the performance of simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), when used for electroencephalographic (EEG) source localization. Such task can be posed as an optimization problem for which the referred metaheu...

In this paper, we compare the performance of brain-computer interfaces (BCIs) when different feedback modalities are used. In particular, we study the effects of auditory or vibrotactile feedback when presented to reinforce the users' performance (positive feedback) or to correct a poor achievement in controlling the BCI (negative feedback). Then,...

We present a source localization method for electroencephalographic (EEG) and magnetoencephalographic (MEG) data which is based on an estimate of the sparsity obtained through the eigencanceler (EIG), which is a spatial filter whose weights are constrained to lie in the noise subspace. The EIG provides rejection of directional interferences while m...

We consider the problem of dipole source signals estimation in electroencephalography (EEG) using beamforming techniques in ill-conditioned settings. We take advantage of the link between the linearly constrained minimum-variance (LCMV) beamformer in sensor array processing and the best linear unbiased estimator (BLUE) in linear regression modeling...

The problem of electroencephalographic (EEG) source localization involves an optimization process that can be solved through metaheuristics. In this paper, we evaluate the performance in localizing EEG dipole sources using simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). The evalu...

A processing framework is proposed to model relative changes in fetal sympatho-vagal balance at equally spaced gestational periods. The proposed method is based on a multivariable time-varying autoregression (TVAR) of the beat-to-beat time differences obtained from non-invasive fetal electrocardiographic (ECG) or magnetocardiographic (MCG) measurem...

We present a solution of the electroencephalographic (EEG) forward problem for the case when the head's geometry is modeled using a four-shell ellipsoidal geometry and the source is a current dipole. The EEG potentials generated by this forward model have been previously approximated with elliptic integrals and harmonics up to second-order. Here, w...

We present a source localization method for electroencephalographic (EEG) data based on the linearly constrained minimum variance and eigencanceler beamformers. A region-of-interest (ROI) is selected through a short-term estimate of the signal's energy as constraint. Such constraint is only valid on the scalp, then an affine transformation is appli...

The problem of electroencephalographic (EEG) source localization involves an optimization problem that can be solved through global optimization methods. In this paper, we evaluate the performance in localizing EEG sources of simulated annealing (SA) and genetic algorithm (GA) as a function of the optimization's initialization parameters and the si...

The performance of EEG signal classification methods based on Common Spatial Patterns (CSP) depends on the operational frequency bands of the events to be discriminated. This problem has been recently addressed by using a sub-band decomposition of the EEG signals through filter banks. Even though this approach has proven effective, the performance...

In this paper, we evaluate the performance of simulated annealing (SA) and the genetic algorithm (GA) when used for electroencephalographic (EEG) source localization. The performance is evaluated on the variance of the estimated localizations as a function of the optimization's initialization parameters and the signal-to-noise ratio (SNR). We use t...

The purpose of this preliminary work is to evaluate the effectiveness of the single/multi-channel energy transform (ET) as preprocessing tool for magnetoencephalographic (MEG) data-based applications. The ET is a derivative-based transformation that enhances either the variability content of a signal from a single channel, or the compound variabili...

We propose a method to analyze fetal beat-to-beat heart rate variability (HRV) obtained from magnetocardiographic (MCG) measurements at different times of gestation using a multivariate time-varying autoregressive (MTVAR) model. Our approach is based on treating a group of HRV signals from a single fetus and measured at different gestational period...

This paper presents a preprocessing technique for improving the classification of electroencephalographic (EEG) data in brain-computer interfaces (BCI) for the case of realistic measuring conditions, such as low signal-to-noise ratio (SNR), reduced number of measuring electrodes, and reduced amount of data used to train the classifier. The proposed...

We present forward modeling solutions in the form of array response kernels for electroencephalography (EEG) and magnetoencephalography (MEG), assuming that a multilayer ellipsoidal geometry approximates the anatomy of the head and a dipole current models the source. The use of an ellipsoidal geometry is useful in cases for which incorporating the...

In this paper we characterize fetal sympatho-vagal balance by studying the correlation, time shifts, and ratios, between very-low-frequency (VLF), low-frequency (LF), and high-frequency (HF) components in heart rate (HR) variability over time. The HR is obtained from fetal magnetocardiographic (fMCG) recordings at various gestational periods. The f...

We present an analytical forward modeling solution in the form of an array response kernel for electroencephalography (EEG) assuming a four-shell ellipsoidal geometry that approximates the anatomy of the brain, cerebrospinal fluid (CSF), skull, and scalp, while a current dipole models the source. The use of an ellipsoidal geometry is useful for cas...

We present analytical forward modeling solutions in the form of array response kernels for electroencephalography (EEG) and magnetoencephalography (MEG) assuming a single-shell ellipsoidal geometry that approximates the anatomy of the head and a dipole current models the source. The structure of our solution facilitates the analysis of the inverse...

We propose the use of length and energy transforms in the classification of multichannel EEG data to identify different cognitive activity using a reduced set of recording electrodes. The length transform (ET) represents a temporarily smoothed time course of the data, while the energy transform (ET) can be interpreted as a short-term energy estimat...

Fetal magnetoencephalography (fMEG) is a non-invasive technique where measurements of the magnetic field outside the maternal abdomen are used to infer the source location and signals of the fetus' neural activity. There are a number of aspects related to fMEG modelling that must be addressed, such as the conductor volume, fetal position and orient...

Current standard magnetoencephalographic and -cardiographic systems do not allow real-time access to the measured data. We developed a software solution for real-time access and used it to create an online fetal heart rate monitor.

We estimate a source current dipole at a known location in the presence of low-rank interference using magnetoencephalography (MEG). We present a space-time processor for MEG data based on the generalized sidelobe canceler (GSC). We extend the classical vector beamformer to a matrix structure without making any assumptions on the rank of the covari...

We estimate a source current dipole at a known location in the presence of low-rank interference using magnetoencephalography (MEG). We present a space-time processor for MEG data based on the generalized sidelobe canceler (GSC). We extend the classical vector beamformer to a matrix structure without making any assumptions on the rank of the covari...

Objective: Fetal magnetoencephalography (fMEG) is the only complete non-invasive method to record fetal brain signals in the utero. When using this method in a clinical setting, it is necessary to monitor the fetal heart rate during the recordings. Based on the high sensitivity of the magnetic sensors used, it is not possible to record the heart ra...