BUSCADOR RECOLECTA

Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies., This work was supported by German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115 and 01GQ0850; Deutsche Forschungsgesellschaft (DFG) under Grant MU 987/19-1, MU987/14-1 and DFG MU 987/3-2; Brain Korea 21 Plus Program and by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451); and Spanish Ministry of Economy RYC-2014-15671.

Trabajo presentado a la 15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020 (11-13 de noviembre de 2020), 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., This research was supported by MINECO (RYC-2014-15671).

Brain-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., CV was supported by MINECO-RyC-2014-15671. SH was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 758985). K-RM was supported in part by the Institute for Information & Communications Technology Promotion and funded by the Korea government (MSIT) (No. 2017-0-00451), and was partly supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A, and 01IS18037A; the German ResearchFoundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689. VVN was partly supported by the HSE Basic Research Program and the Russian Academic Excellence Project 5-100.

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., TJ was partly supported by the European Erasmus + Program for international mobility within Campus Iberus. VVN was supported in part by the Basic Research Program at the National Research University Higher School of Economics (HSE University). CV was supported by MINECO-RyC-2014–15671.

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., C V was supported by MINECO-RyC-2014-15671 and PID2020-118829RB-I00. A R was supported by EU-EUROSTARS E!113550 and H2020-EICFETPROACT-2019-951910-MAIA. K R M was supported in part by the Institute of Information & Communications Technology Planning & Evaluation (IITP) Grants funded by the Korea Government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning) and funded by the Korea Government (No. 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University), and was partly supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A and 01IS18037A; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689. VVN was partly supported by the Basic Research Program of the National Research University Higher School of Economics (HSE University).

In this paper we propose a new version of penalty-based aggregation functions, the Multi Cost Aggregation choosing functions (MCAs), in which the function to minimize is constructed using a convex combination of two relaxed versions of restricted equivalence and dissimilarity functions instead of a penalty function. We additionally suggest two different alternatives to train a MCA in a supervised classification task in order to adapt the aggregation to each vector of inputs. We apply the proposed MCA in a Motor Imagery-based Brain- Computer Interface (MI-BCI) system to improve its decision making phase. We also evaluate the classical aggregation with our new aggregation procedure in two publicly available datasets. We obtain an accuracy of 82.31% for a left vs. right hand in the Clinical BCI challenge (CBCIC) dataset, and a performance of 62.43% for the four-class case in the BCI Competition IV 2a dataset compared to a 82.15% and 60.56% using the arithmetic mean. Finally, we have also tested the goodness of our proposal against other MI-BCI systems, obtaining better results than those using other decision making schemes and Deep Learning on the same datasets., Javier Fumanal Idocin, Javier Fernandez, and Humberto Bustince's
research has been supported by the project PID2019-108392GB I00
(AEI/10.13039/501100011033).
Carmen Vidaurre research has been funded by the project RyC2014-15671.

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., La investigación de Javier Fumanal Idocin, A.Urio y
Humberto Bustince ha sido apoyada por el proyecto
PID2019-108392GB I00 (AEI / 10.13039 / 501100011033).
La investigación de Carmen Vidaurre ha sido financiada por
el proyecto RyC-2014-15671.