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
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
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.
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.
Open Access
A fast SSVEP-based brain-computer interface
(Springer, 2020) Jorajuria Gómez, Tania; Gómez Fernández, Marisol; Vidaurre Arbizu, 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
Improving motor imagery classification during induced motor perturbations
(IOP Publishing, 2021) Vidaurre Arbizu, Carmen; Jorajuria Gómez, Tania; Ramos Murguialday, Ander; Müller, Klaus Robert; Gómez Fernández, Marisol; Nikulin, Vadim V.; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Objective. Motor imagery is the mental simulation of movements. It is a common paradigm to design brain-computer interfaces (BCIs) that elicits the modulation of brain oscillatory activity similar to real, passive and induced movements. In this study, we used peripheral stimulation to provoke movements of one limb during the performance of motor imagery tasks. Unlike other works, in which induced movements are used to support the BCI operation, our goal was to test and improve the robustness of motor imagery based BCI systems to perturbations caused by artificially generated movements. Approach. We performed a BCI session with ten participants who carried out motor imagery of three limbs. In some of the trials, one of the arms was moved by neuromuscular stimulation. We analysed 2-class motor imagery classifications with and without movement perturbations. We investigated the performance decrease produced by these disturbances and designed different computational strategies to attenuate the observed classification accuracy drop. Main results. When the movement was induced in a limb not coincident with the motor imagery classes, extracting oscillatory sources of the movement imagination tasks resulted in BCI performance being similar to the control (undisturbed) condition; when the movement was induced in a limb also involved in the motor imagery tasks, the performance drop was significantly alleviated by spatially filtering out the neural noise caused by the stimulation. We also show that the loss of BCI accuracy was accompanied by weaker power of the sensorimotor rhythm. Importantly, this residual power could be used to predict whether a BCI user will perform with sufficient accuracy under the movement disturbances. Significance. We provide methods to ameliorate and even eliminate motor related afferent disturbances during the performance of motor imagery tasks. This can help improving the reliability of current motor imagery based BCI systems.