Person:
Gómez Fernández, Marisol

Last Name

Gómez Fernández

First Name

Marisol

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Matemática e Informática

ORCID

0000-0003-3431-1256

person.page.upna

279

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Now showing 1 - 10 of 13

Open Access
Relevance of sex, age and gait kinematics when predicting fall-risk and mortality in older adults
(Elsevier, 2020) Porta Cuéllar, Sonia; Martínez Ramírez, Alicia; Millor Muruzábal, Nora; Gómez Fernández, Marisol; Izquierdo Redín, Mikel; Ingeniería Eléctrica, Electrónica y de Comunicación; Estadística, Informática y Matemáticas; Ciencias de la Salud; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Estatistika, Informatika eta Matematika; Osasun Zientziak; Gobierno de Navarra / Nafarroako Gobernua, 87/10
Approximately one-third of elderly people fall each year with severe consequences, including death. The aim of this study was to identify the most relevant features to be considered to maximize the accuracy of a logistic regression model designed for prediction of fall/mortality risk among older people. This study included 261 adults, aged over 65 years. Men and women were analyzed separately because sex stratification was revealed as being essential for our purposes of feature ranking and selection. Participants completed a 3-m walk test at their own gait velocity. An inertial sensor attached to their lumbar spine was used to record acceleration data in the three spatial directions. Signal processing techniques allowed the extraction of 21 features representative of gait kinematics, to be used as predictors to train and test the model. Age and gait speed data were also considered as predictors. A set of 23 features was considered. These features demonstrate to be more or less relevant depending on the sex of the cohort under analysis and the classification label (risk of falls and mortality). In each case, the minimum size subset of relevant features is provided to show the maximum accuracy prediction capability. Gait speed has been largely used as the single feature for the prediction fall risk among older adults. Nevertheless, prediction accuracy can be substantially improved, reaching 70% in some cases, if the task of training and testing the model takes into account some other features, namely, sex, age and gait kinematic parameters. Therefore we recommend considering sex, age and step regularity to predict fall-risk.
Open Access
A fast SSVEP-based brain-computer interface
(Springer, 2020) Jorajuría, Tania; Gómez Fernández, Marisol; Vidaurre, Carmen; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Literature of brain-computer interfacing (BCI) for steady-state visual evoked potentials (SSVEP) shows that canonical correlation analysis (CCA) is the most used method to extract features. However, it is known that CCA tends to rapidly overfit, leading to a decrease in performance. Furthermore, CCA uses information of just one class, thus neglecting possible overlaps between different classes. In this paper we propose a new pipeline for SSVEP-based BCIs, called corrLDA, that calculates correlation values between SSVEP signals and sine-cosine reference templates. These features are then reduced with a supervised method called shrinkage linear discriminant analysis that, unlike CCA, can deal with shorter time windows and includes between-class information. To compare these two techniques, we analysed an open access SSVEP dataset from 24 subjects where four stimuli were used in offline and online tasks. The online task was performed both in control condition and under different perturbations: listening, speaking and thinking. Results showed that corrLDA pipeline outperforms CCA in short trial lengths, as well as in the four additional noisy conditions.
Open Access
Initiative to increment the number of women in STEM degrees: women, science and technology chair of the Public University of Navarre
(IEEE, 2020) Aranguren Garacochea, Patricia; San Martín Biurrun, Idoia; Catalán Ros, Leyre; Martínez Ramírez, Alicia; Jurío Munárriz, Aránzazu; Díaz Lucas, Silvia; Pérez Artieda, Miren Gurutze; Gómez Fernández, Marisol; Barrenechea Tartas, Edurne; Estadística, Informática y Matemáticas; Ingeniería; Ingeniería Eléctrica, Electrónica y de Comunicación; Estatistika, Informatika eta Matematika; Ingeniaritza; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Gobierno de Navarra / Nafarroako Gobernua; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
The Public University of Navarre joined with Navarre Government has created the Women, Science and Technology Chair. This chair arises due to the plummeting tendency of the percentage of women in STEM degrees with the aim of reversing this trend. The programme of activities is defined throughout this contribution by six activities: a Theatre Play, a Poster Award on Final Degree/Masters Project, The 1st Week of Women, Science and Technology, the Promotion of Technical Degrees in schools and high-schools, a Workshop about Gender Stereotypes and the Fostering of Women among Science and Environment. Each activity gained great success and the preset goals were highly accomplished, especially, the 1st Week of Women, Science and Technology activity. The latter achieved a great success both in participation and in repercussion, contributing to visualize the role of women in science and technology.
Open Access
MEANSP: How many channels are needed to predict the performance of a SMR-Based BCI?
(IEEE, 2023) Jorajuría, Tania; Nikulin, Vadim V.; Kapralov, Nikolai; Gómez Fernández, Marisol; Vidaurre, Carmen; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Predicting whether a particular individual would reach an adequate control of a Brain-Computer Interface (BCI) has many practical advantages. On the one hand, participants with low predicted performance could be trained with specifically designed sessions and avoid frustrating experiments; on the other hand, planning time and resources would be more efficient; and finally, the variables related to an accurate prediction could be manipulated to improve the prospective BCI performance. To this end, several predictors have been proposed in the literature, most of them based on the power estimation of EEG signals at the specific frequency bands. Many of these studies evaluate their predictors in relatively small datasets and/or using a relatively high number of channels. In this manuscript, we propose a novel predictor called MEANSP to predict the performance of participants using BCIs that are based on the modulation of sensorimotor rhythms. This novel predictor has been positively evaluated using only 2, 3, 4 or 5 channels. MEANSP has shown to perform as well as or better than other state-of-the-art predictors. The best sets of different number of channels are also provided, which have been tested in two different settings to prove their robustness. The proposed predictor has been successfully evaluated using two large-scale datasets containing 150 and 80 participants, respectively. We also discuss predictor thresholds for users to expect good performance in feedback experiments and show the advantages in comparison to a competing algorithm.
Open Access
Sources of linear and non-linear synchrony between brain and muscles: linear and non-linear CMC sources
(IEEE, 2020) Vidaurre, Carmen; Gómez Fernández, Marisol; Nolte, Guido; Villringer, Arno; Carlowitz Ghori, Katherina von; Nikulin, Vadim V.; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
This manuscript shows that it is possible to find distinct sources of brain activity, at similar frequencies, arising from linear and non-linear interactions of the brain with the muscular system. Those sources were obtained by maximizing coherence between multivariate signals recorded from brain and a single channel from the muscles. To find linear phase synchrony we used unrectified electromyographic recordings, whereas to de-mix nonlinear sources, we used rectified muscular measurements. In order to obtain the brain sources, we employed a recently published method called 'cacoh' that is able to maximize coherence over the complete frequency range of interest and simultaneously find patterns of sources for each them. Our results show that cortico-muscular interactions even at the same frequency range can have spatially distinct neuronal sources depending on whether interactions had linear or non-linear character.
Open Access
Optical system based on multiplexed FBGs to monitor hand movements
(IEEE, 2021) Socorro Leránoz, Abián Bentor; Díaz Lucas, Silvia; Castillo, Silvia; Dreyer, Uilian José; Martelli, Cicero; Cardozo da Silva, Jean Carlos; Uzqueda Esteban, Itziar; Gómez Fernández, Marisol; Ruiz Zamarreño, Carlos; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC; Institute for Advanced Materials and Mathematics - INAMAT2; Ingeniería Eléctrica, Electrónica y de Comunicación; Estadística, Informática y Matemáticas; Gobierno de Navarra / Nafarroako Gobernua
This contribution reports the development and characterization of an optical system based on parallel Fiber Bragg Gratings (FBGs) to monitor the movements of the wrist and fingers of a hand. The system consisted of a reflective configuration made of FBGs detecting the movements of the fingers and one more located on the wrist as a reference. All FBGs were multiplexed in order to collect the basic movements of the hand. Fibers were embedded in polydimethylsiloxane for protection and to give flexibility to the optical detection setup. Measurements of strain, angle and torsion were performed during the experiments, obtaining sensitivities up to 1.29 pm/ \mu \varepsilon in strain and 64.23 pm/° in angle. Also, a study on the influence of a single sensor on the performance of the whole system was analyzed for a complete study of this proof of concept. The obtained results present a simple system that can be used to monitor the positions of the hand or for the rehabilitation of patients suffering from neuromotor or post-stroke diseases.
Open Access
Oscillatory source tensor discriminant analysis (OSTDA): a regularized tensor pipeline for SSVEP-based BCI systems
(Elsevier, 2021) Jorajuría, Tania; Jamshidi Idaji, Mina; İşcan, Zafer; Gómez Fernández, Marisol; Nikulin, Vadim V.; Vidaurre, Carmen; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Periodic signals called Steady-State Visual Evoked Potentials (SSVEP) are elicited in the brain by flickering stimuli. They are usually detected by means of regression techniques that need relatively long trial lengths to provide feedback and/or sufficient number of calibration trials to be reliably estimated in the context of brain-computer interface (BCI). Thus, for BCI systems designed to operate with SSVEP signals, reliability is achieved at the expense of speed or extra recording time. Furthermore, regardless of the trial length, calibration free regression-based methods have been shown to suffer from significant performance drops when cognitive perturbations are present affecting the attention to the flickering stimuli. In this study we present a novel technique called Oscillatory Source Tensor Discriminant Analysis (OSTDA) that extracts oscillatory sources and classifies them using the newly developed tensor-based discriminant analysis with shrinkage. The proposed approach is robust for small sample size settings where only a few calibration trials are available. Besides, it works well with both low- and high-number-of-channel settings, using trials as short as one second. OSTDA performs similarly or significantly better than other three benchmarked state-of-the-art techniques under different experimental settings, including those with cognitive disturbances (i.e. four datasets with control, listening, speaking and thinking conditions). Overall, in this paper we show that OSTDA is the only pipeline among all the studied ones that can achieve optimal results in all analyzed conditions.
Open Access
Women, Science and Technology Chair—Promoting women’s careers in stem fields
(IEEE, 2023) Pérez Artieda, Miren Gurutze; Gómez Fernández, Marisol; Aranguren Garacochea, Patricia; Barrenechea Tartas, Edurne; Catalán Ros, Leyre; Díaz Lucas, Silvia; Jurío Munárriz, Aránzazu; Martínez Ramírez, Alicia; Millor Muruzábal, Nora; Ortiz Nicolás, Amalia; San Martín Biurrun, Idoia; Estadística, Informática y Matemáticas; Ingeniería; Ingeniería Eléctrica, Electrónica y de Comunicación; Estatistika, Informatika eta Matematika; Ingeniaritza; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
The Chair of Women, Science and Technology of the Universidad Pública de Navarra (UPNA) aims to increase the participation of women in the fields of science and technology. Scientific culture and dissemination are the main focus of the different actions of the Chair. These activities include: the theatrical performance "Yo quiero ser científica", experimental workshops and conferences and exhibitions for all audiences and ages. More than 6.000 people have seen the play, more than 1.500 secondary school students have participated in the workshops and the audiovisual material has received more than 20.000 visits.
Embargo
Degree of totalness: how to choose the best admissible permutation for vector fuzzy integration
(Elsevier, 2023) Ferrero Jaurrieta, Mikel; Horanská, Lubomíra; Lafuente López, Julio; Mesiar, Radko; Pereira Dimuro, Graçaliz; Takáč, Zdenko; Gómez Fernández, Marisol; Fernández Fernández, Francisco Javier; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
The use of aggregation operators that require ordering of the data brings a problem when the structures to be aggregated are multi-valued, since there may be several admissible orders. To addressing this problem, the concept of admissible permutation was introduced for intervals. In this paper we extend this concept to vector domain. However, the problem of selecting the best possible permutation is still an open problem. In this paper we present a novel concept in order to choose the best admissible permutation for vectors: the degree of totalness. This concept allows us to represent to which degree the admissible permutation reorder given vectors as a chain with respect to the partial order. Finally, from the best admissible permutation we construct the Choquet integral.
Open Access
Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings
(Elsevier, 2023) Vidaurre, Carmen; Gurunandan, Kshipra; Jamshidi Idaji, Mina; Nolte, Guido; Gómez Fernández, Marisol; Villringer, Arno; Müller, Klaus Robert; Nikulin, Vadim V.; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Instantaneous and peak frequency changes in neural oscillations have been linked to many perceptual, motor, and cognitive processes. Yet, the majority of such studies have been performed in sensor space and only occasionally in source space. Furthermore, both terms have been used interchangeably in the literature, although they do not reflect the same aspect of neural oscillations. In this paper, we discuss the relation between instantaneous frequency, peak frequency, and local frequency, the latter also known as spectral centroid. Furthermore, we propose and validate three different methods to extract source signals from multichannel data whose (instantaneous, local, or peak) frequency estimate is maximally correlated to an experimental variable of interest. Results show that the local frequency might be a better estimate of frequency variability than instantaneous frequency under conditions with low signal-to-noise ratio. Additionally, the source separation methods based on local and peak frequency estimates, called LFD and PFD respectively, provide more stable estimates than the decomposition based on instantaneous frequency. In particular, LFD and PFD are able to recover the sources of interest in simulations performed with a realistic head model, providing higher correlations with an experimental variable than multiple linear regression. Finally, we also tested all decomposition methods on real EEG data from a steady-state visual evoked potential paradigm and show that the recovered sources are located in areas similar to those previously reported in other studies, thus providing further validation of the proposed methods.