Open Access
Oscillatory source tensor discriminant analysis (OSTDA): a regularized tensor pipeline for SSVEP-based BCI systems
(Elsevier, 2021) Jorajuria Gómez, Tania; Jamshidi Idaji, Mina; İşcan, Zafer; Gómez Fernández, Marisol; Nikulin, Vadim V.; Vidaurre Arbizu, 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
An evaluation of the 30-s chair stand test in older adults: frailty detection based on kinematic parameters from a single inertial unit
(BioMed Central, 2013) Millor Muruzábal, Nora; Lecumberri Villamediana, Pablo; Gómez Fernández, Marisol; Martínez Ramírez, Alicia; Izquierdo Redín, Mikel; Matemáticas; Ciencias de la Salud; Matematika; Osasun Zientziak; Gobierno de Navarra / Nafarroako Gobernua
Background: A growing interest in frailty syndrome exists because it is regarded as a major predictor of co-morbidities and mortality in older populations. Nevertheless, frailty assessment has been controversial, particularly when identifying this syndrome in a community setting. Performance tests such as the 30-second chair stand test (30-s CST) are a cornerstone for detecting early declines in functional independence. Additionally, recent advances in body-fixed sensors have enhanced the sensors’ ability to automatically and accurately evaluate kinematic parameters related to a specific movement performance. The purpose of this study is to use this new technology to obtain kinematic parameters that can identify frailty in an aged population through the performance the 30-s CST. Methods: Eighteen adults with a mean age of 54 years, as well as sixteen pre-frail and thirteen frail patients with mean ages of 78 and 85 years, respectively, performed the 30-s CST while threir trunk movements were measured by a sensor-unit at vertebra L3. Sit-stand-sit cycles were determined using both acceleration and orientation information to detect failed attempts. Movement-related phases (i.e. impulse, stand-up, and sit-down) were differentiated based on seat off and seat on events. Finally, the kinematic parameters of the impulse, stand-up and sit-down phases were obtained to identify potential differences across the three frailty groups. Results: For the stand-up and sit-down phases, velocity peaks and “modified impulse” parameters clearly differentiated subjects with different frailty levels (p < 0.001). The trunk orientation range during the impulse phase was also able to classify a subject according to his frail syndrome (p < 0.001). Furthermore, these parameters derived from the inertial units (IUs) are sensitive enough to detect frailty differences not registered by the number of completed cycles which is the standard test outcome. Conclusions: This study shows that IUs can enhance the information gained from tests currently used in clinical practice, such as the 30-s CST. Parameters such as velocity peaks, impulse, and orientation range are able to differentiate between adults and older populations with different frailty levels. This study indicates that early frailty detection could be possible in clinical environments, and the subsequent interventions to correct these disabilities could be prescribed before further degradation occurs.
Open Access
Sources of linear and non-linear synchrony between brain and muscles: linear and non-linear CMC sources
(IEEE, 2020) Vidaurre Arbizu, 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
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.
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
Do frailty and cognitive impairment affect dual-task cost during walking in the oldest old institutionalized patients?
(Springer, 2015-12-14) Lusa Cadore, Eduardo; Casas Herrero, Álvaro; Zambom Ferraresi, Fabrício; Martínez Ramírez, Alicia; Millor Muruzábal, Nora; Gómez Fernández, Marisol; Bays Moneo, Ana Beatriz; Izquierdo Redín, Mikel; Ciencias de la Salud; Osasun Zientziak; Matemáticas; Matematika
Open Access
Cátedra Mujer, Ciencia y Tecnología de la UPNA
(Gobierno de Navarra, 2023) Aranguren Garacochea, Patricia; Barrenechea Tartas, Edurne; Catalán Ros, Leyre; Díaz Lucas, Silvia; Jurío Munárriz, Aránzazu; Martínez Ramírez, Alicia; Millor Muruzábal, Nora; Gómez Fernández, Marisol; San Martín Biurrun, Idoia; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Ingeniería; Ingeniaritza; Institute of Smart Cities - ISC; Institute for Advanced Materials and Mathematics - INAMAT2
La Cátedra Mujer, Ciencia y Tecnología de la Universidad Pública de Navarra (UPNA) tiene como objetivo aumentar la participación de las mujeres en campos de ciencia y tecnología. La cultura y la divulgación científicas son el eje principal de la actividad de la Cátedra. Dicha actividad engloba: la representación teatral Yo quiero ser científica, talleres experimentales y conferencias y exposiciones para todos los públicos y edades. Más de 6000 personas han visto la obra de teatro, más de 1500 estudiantes de ESO han participado en los talleres y el material audiovisual ha recibido más de 20000 visitas.
Embargo
Enhancing sensorimotor BCI performance with assistive afferent activity: an online evaluation
(Elsevier, 2019) Vidaurre Arbizu, Carmen; Ramos Murguialday, Ander; Haufe, Stefan; Gómez Fernández, Marisol; Müller, Klaus Robert; Nikulin, Vadim V.; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
An important goal in Brain-Computer Interfacing (BCI) is tofind and enhance procedural strategies for users for whom BCI control is not sufficiently accurate. To address this challenge, we conducted offline analyses and online experiments to test whether the classification of different types of motor imagery could be improved when the training of the classifier was performed on the data obtained with the assistive muscular stimulation below the motor threshold. 10 healthy participants underwent three different types of experimental conditions: a) Motor imagery (MI) of hands and feet b) sensory threshold neuromuscular electrical stimulation (STM) of hands and feet while resting and c) sensory threshold neuromuscular electrical stimulation during performance of motor imagery (BOTH). Also, another group of 10 participants underwent conditions a) and c). Then, online experiments with 15 users were performed. These subjects received neurofeedback during MI using classifiers calibrated either on MIor BOTH data recorded in the same experiment. Offline analyses showed that decoding MI alone using a classifier based on BOTH resulted in a better BCI accuracy compared to using a classifier based on MI alone. Online experiments confirmed accuracy improvement of MI alone being decoded with the classifier trained on BOTH data. In addition, we observed that the performance in MI condition could be predicted on the basis of a more pronounced connectivity within sensorimotor areas in the frequency bands providing the best performance in BOTH. Thesefinding might offer a new avenue for training SMR-based BCI systems particularly for users having difficulties to achieve efficient BCI control. It might also be an alternative strategy for users who cannot perform real movements but still have remaining afferent pathways (e.g., ALS and stroke patients).
Open Access
MEANSP: How many channels are needed to predict the performance of a SMR-Based BCI?
(IEEE, 2023) Jorajuria Gómez, Tania; Nikulin, Vadim V.; Kapralov, Nikolai; Gómez Fernández, Marisol; Vidaurre Arbizu, 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.