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
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
EEG-based endogenous online co-adaptive brain-computer interfaces: strategy for success?
(IEEE, 2018) Scherer, Reinhold; Faller, Josef; Sajda, Paul; Vidaurre Arbizu, Carmen; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
A Brain-Computer Interface (BCI) translates patterns of brain signals such as the electroencephalogram (EEG) into messages for communication and control. In the case of endogenous systems the reliable detection of induced patterns is more challenging than the detection of the more stable and stereotypical evoked responses. In the former case specific mental activities such as motor imagery are used to encode different messages. In the latter case users have to attend sensory stimuli to evoke a characteristic response. Indeed, a large number of users who try to control endogenous BCIs do not reach sufficient level of accuracy. This fact is also known as BCI “inefficiency” or “illiteracy”. In this paper we discuss and make some conjectures, based on our knowledge and experience in BCI, on whether or not online co-adaptation of human and machine can be the solution to overcome this challenge. We point out some ingredients that might be necessary for the system to be reliable and allow the users to attain sufficient control.
Open Access
Optimizando desviaciones moderadas ponderadas para interfaces cerebro ordenador
(Universidad de Málaga, 2021) Fumanal Idocin, Javier; Vidaurre Arbizu, Carmen; Gómez Fernández, Marisol; Urío Larrea, Asier; Pereira Dimuro, Graçaliz; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika
Las interfaces cerebro-ordenador (BCI) basadas en el análisis de Electroencefalografía (EEG) están compuestas por varios elementos para procesar y clasificar las señales de entrada del cerebro. Una fase relevante de estos sistemas es el módulo de toma de decisiones, en el que la salida de diferentes clasificadores se fusiona en uno solo. En este trabajo proponemos el uso de funciones basadas en desviaciones moderadas con ponderaciones para la fase de toma de decisiones del sistema de BCI de fusión multimodal mejorado (EMF). Las funciones de agregación basadas en desviación moderada (MD) nos permiten elegir el mejor valor para agregar un vector de puntos utilizando una función de desviación moderada. Usando una MD ponderada, también podemos tener en cuenta la importancia relativa de cada dimensión en los datos multidimensionales que estamos agregando. Utilizando estas funciones en el EMF, podemos ponderar cada una de las diferentes señales cerebrales según su importancia, y utilizando la diferenciación automática, también podemos optimizarlas para el problema concreto a solucionar.

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