Person: Jorajuria Gómez, Tania
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Jorajuria Gómez
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Tania
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Estadística, Informática y Matemáticas
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0000-0002-6493-7770
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811127
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Publication Open Access Sensorimotor functional connectivity: a neurophysiological factor related to BCI performance(Frontiers Media, 2020) Vidaurre Arbizu, Carmen; Haufe, Stefan; Jorajuria Gómez, Tania; Müller, Klaus Robert; Nikulin, Vadim V.; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaBrain-computer interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. About 20% of potential users of sensorimotor BCIs do not gain reliable control of the system. The inefficiency to decode user's intentions requires the identification of neurophysiological factors determining 'good' and 'poor' BCI performers. One of the important neurophysiological aspects in BCI research is that the neuronal oscillations, used to control these systems, show a rich repertoire of spatial sensorimotor interactions. Considering this, we hypothesized that neuronal connectivity in sensorimotor areas would define BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in a calibration and an online feedback session recorded on the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post- as well as pre-stimulus connectivity in the calibration recording is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study demonstrates that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sensorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.Publication 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 MatematikaLiterature 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.Publication Open Access Evolutionary algorithms applied to multi-layered radiative cooling metamaterials(IEEE, 2022) Lezaun Capdevila, Carlos; Jorajuria Gómez, Tania; Torres García, Alicia E.; Herrera, Pilar; Beruete Díaz, Miguel; Ingeniería Eléctrica, Electrónica y de Comunicación; Institute of Smart Cities - ISC; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio IngeniaritzarenA newly design method for designing multi-layered radiative cooling metamaterials based on genetic algorithms (GAs) is exposed. The developed GA has been tested in three cases, resulting in three different structures that achieve, theoretically under direct sunlight, a net cooling power of 39.96 W/m 2 , 57.78 W/m 2 and 61.77 W/m 2 . Such devices are composed of 9, 15 and 24 layers respectively with a total thickness of less than 4.8 µm in the worst case. By the nature of the method, fewer design experience in metamaterials is needed, as well as it is free-cost, due to the use of analytical calculations for the emissivity of the meta materials instead of a commercial generic electromagnetic solver. Automated design of radiative cooling multi-layered structures and other applications in the infrared range can be further developed with this work.Publication Open Access Design of multi-layered radiative cooling structures using evolutionary algorithms(IEEE, 2022) Lezaun Capdevila, Carlos; Jorajuria Gómez, Tania; Torres García, Alicia E.; Herrera, Pilar; Beruete Díaz, Miguel; Institute of Smart Cities - ISC; Ingeniería Eléctrica y Electrónica; Ingeniaritza Elektrikoa eta Elektronikoa; Gobierno de Navarra / Nafarroako GobernuaIn this work we present a novel way to design thinfilm radiative cooling metamaterials based on genetic algorithms. Three simulations with different design constraints have been done, resulting in three structures that achieve 39.96 W/m2 , 57.78 W/m2 and 61.77 W/m2 under direct sunlight, respectively. These structures are shorter than 5 µm of height and are composed of 9, 15 and 24 layers. This design method has the advantages of being automatable, needs fewer design experience in metamaterials and does not rely on commercial simulators. This work opens the path to an easy way of automated design of thin-film multi-layered devices for radiative cooling and other applications in the infrared range.Publication 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 MatematikaPredicting 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.Publication 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 MatematikaPeriodic 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.Publication Open Access Toward robustness and performance prediction in Brain-Computer Interfacing(2024) Jorajuria Gómez, Tania; Gómez Fernández, Marisol; Vidaurre, Carmen; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaLas interfaces cerebro-computadora (BCIs, por sus siglas en inglés) leen y decodifican la actividad cerebral. Entre sus muchas aplicaciones, se pueden emplear como una nueva herramienta de comunicación, para controlar un dispositivo externo, asistir a pacientes durante neurorrehabilitación, o incluso en neuromarketing. Las BCIs normalmente se prueban bajo condiciones controladas. Sin embargo, su correcta operación en condiciones de fuera del laboratorio es crucial, especialmente cuando los investigadores tienen en mente su uso en aplicaciones clínicas. Por tanto, es vital que las BCIs sean robustas a perturbaciones externas. Por otro lado, se ha demostrado que un gran porcentaje de usuarios no puede operar correctamente las BCIs actuales. Este fenómeno, conocido como ineficiencia de las BCIs, señala la necesidad de que los investigadores estimen de antemano qué rendimiento tendrá un nuevo participante. Predecir el desempeño de un participante podría ahorrar recursos de investigación, que siempre son escasos, y ayudar en el diseño de algoritmos para aumentar la fiabilidad operativa de las BCIs. En esta Tesis, se han abordado los puntos anteriores, dando como resultado la propuesta de nuevas herramientas para el procesamiento de señales BCI. En particular, se han propuesto dos métodos de procesamiento (pipelines) para BCIs basadas en señales steady-state visual evoked potential (SSVEP), que funcionan bien con señales de sólo un segundo de duración. Uno de ellos, denominado corrLDA, supera al pipeline del estado del arte basado en el an´alisis de la correlación canónica (CCA). Su aspecto clave es que, aplicando el análisis discriminante lineal regularizado (sLDA), emplea información de clase para encontrar el subespacio de extracción de características, al contrario que CCA. El siguiente pipeline, llamado oscillatory source tensor discriminant analysis (OSTDA), es una extensión de corrLDA con dos características principales: primero, sLDA se reemplaza por su método análogo para datos tensoriales, denominado análisis discriminante de orden superior regularizado (sHODA), desarrollado durante esta Tesis. Este método de extracción de características basado en tensores convierte a OSTDA en un pipeline robusto en condiciones de tamaños muestrales pequeños. Además, OSTDA también incluye un método llamado descomposición espacio-espectral (SSD). Se aplica como un primer paso en el procesado de las señales SSVEP. SSD extrae fuentes oscilatorias con relación señal-ruido mejorada. De este modo, SSD convierte a OSTDA en un pipeline más robusto contra perturbaciones cognitivas puesto que, a diferencia de las señales SSVEP, las perturbaciones no son de naturaleza oscilatoria y por tanto pueden eliminarse al proyectar los datos al subespacio generado por el SSD. Al comparar OSTDA contra tres métodos del estado del arte, demostró tener un desempeño similar o mejor que ellos en todas las configuraciones estudiadas.Publication 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 MatematikaObjective. 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.