Predicting mental imagery based BCI performance from personality, cognitive profile and neurophysiological patterns

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy— EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants’ BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants’ performance with a mean error of less than 3 points. This study determined how users’ profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user

A machine learning approach to predict perceptual decisions: an insight into face pareidolia

The perception of an external stimulus not only depends upon the characteristics of the stimulus but is also influenced by the ongoing brain activity prior to its presentation. In this work, we directly tested whether spontaneous electrical brain activities in prestimulus period could predict perceptual outcome in face pareidolia (visualizing face in noise images) on a trial-by-trial basis. Participants were presented with only noise images but with the prior information that some faces would be hidden in these images, while their electrical brain activities were recorded; participants reported their perceptual decision, face or no-face, on each trial. Using differential hemispheric asymmetry features based on large-scale neural oscillations in a machine learning classifier, we demonstrated that prestimulus brain activities could achieve a classification accuracy, discriminating face from no-face perception, of 75% across trials. The time–frequency features representing hemispheric asymmetry yielded the best classification performance, and prestimulus alpha oscillations were found to be mostly involved in predicting perceptual decision. These findings suggest a mechanism of how prior expectations in the prestimulus period may affect post-stimulus decision making

Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors

[EN] Affective Computing has emerged as an important field of study that aims to develop systems that can automatically recognize emotions. Up to the present, elicitation has been carried out with nonimmersive stimuli. This study, on the other hand, aims to develop an emotion recognition system for affective states evoked through Immersive Virtual Environments. Four alternative virtual rooms were designed to elicit four possible arousal-valence combinations, as described in each quadrant of the Circumplex Model of Affects. An experiment involving the recording of the electroencephalography (EEG) and electrocardiography (ECG) of sixty participants was carried out. A set of features was extracted from these signals using various state-of-the-art metrics that quantify brain and cardiovascular linear and nonlinear dynamics, which were input into a Support Vector Machine classifier to predict the subject's arousal and valence perception. The model's accuracy was 75.00% along the arousal dimension and 71.21% along the valence dimension. Our findings validate the use of Immersive Virtual Environments to elicit and automatically recognize different emotional states from neural and cardiac dynamics; this development could have novel applications in fields as diverse as Architecture, Health, Education and Videogames.This work was supported by the Ministerio de Economia y Competitividad. Spain (Project TIN2013-45736-R).Marín-Morales, J.; Higuera-Trujillo, JL.; Greco, A.; Guixeres Provinciale, J.; Llinares Millán, MDC.; Scilingo, EP.; Alcañiz Raya, ML.... (2018). Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Scientific Reports. 8:1-15. https://doi.org/10.1038/s41598-018-32063-4S1158Picard, R. W. Affective computing. (MIT press, 1997).Picard, R. W. Affective Computing: Challenges. Int. J. Hum. Comput. 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Growth of vertically aligned carbon nanotubes on aluminium substrate at low temperature through a one-step thermal CVD process

International audienceThis study addresses the vertically aligned carbon nanotubes (VACNT) growth on specific substrates by thermal aerosol assisted CCVD at low temperature (LT). This one-step continuous process is based on the simultaneous injection of catalytic and carbon precursors into a reactor to form in-situ catalytic particles leading to the VACNT growth. It has initially been developed at high temperature (800-850 °C) [Pinault et al 2005, Castro et al 2013] and is easily scalable. Recently it has been adapted to grow VACNT on Al foils to fabricate ultracapacitor electrodes, requesting a lower process temperature [Nassoy thesis 2018]. According to our previous work, hydrogen adjunction in the gas phase promotes the catalyst precursor decomposition at LT. Using acetylene as a carbon precursor is more favorable for decomposition at LT. Recent results enabled to obtain clean, long and dense VACNT at LT with growth rates at the best level of state of the art for multi-step assisted CVD. However, a decrease in growth rate and a catalytic particle poisoning are observed for long time synthesis, inducing a carpet height limitation. The main goal is to strengthen our understanding of VACNT growth at LT and to identify mechanisms involved, in order to have a better control of the growth process

Croissance en une seule étape de nanotubes de carbone vericalement alignés sur des feuilles d'aluminium

International audienceLes tapis de nanotubes de carbone verticalement alignés (VACNT) sont des matériaux aux propriétés structurales, électriques et thermiques très intéressantes pour de nombreuses applications. La méthode de choix pour la synthèse de VACNT de haute qualité est le dépôt chimique en phase vapeur catalytique (CCVD). Cette étude porte sur la croissance de tapis de VACNT sur des substrats d'intérêt par CCVD d'aérosols à basse température. Cette méthode, développée au sein du laboratoire Edifices Nanométriques (LEDNA) consiste à injecter simultanément dans le réacteur un précurseur catalytique et un précurseur carboné de manière à générer in-situ la formation des particules catalytiques à l'origine de la croissance des VACNT. Cette méthode est un procédé de synthèse en continu, en une seule étape, simple, peu coûteux et transposable à grande échelle. Elle a été jusqu'alors développée surtout à haute température (800 à 850°C) [1-3] et récemment elle a été ajustée à la croissance sur aluminium, pour la fabrication d'électrodes de supercondensateurs, qui imposait des températures plus basses de l'ordre de 600°C [4,5]. En se basant sur des travaux antérieurs, notre procédé a été modifié par l'adjonction d'hydrogène en phase gazeuse pour favoriser la décomposition du précurseur catalytique (ferrocène) à basse température [2], et par le remplacement du précurseur carboné liquide (toluène) par l'acétylène, facile à décomposer à basse température [6]. Les résultats récents mettent en évidence une croissance de nanotubes alignés et denses à ces faibles températures avec des vitesses de croissance qui sont comparables à celles obtenues dans l'état de l'art pour des méthodes en deux étapes et assistées (plasma [7] ou filaments chauds [8]). Toutefois, une diminution de la vitesse de croissance en fonction de la durée de synthèse se traduisant par une limitation de la hauteur des tapis de VACNT a été observée [9,10]. Dans ce contexte, l'objectif principal est d'approfondir notre compréhension de la croissance des VACNT spécifiquement à basse température et d'identifier les mécanismes mis en jeu. L'enjeu étant un meilleur contrôle du procédé de croissance dans ces conditions où les phénomènes physico-chimiques à l'oeuvre peuvent être modifiés ou ralentis

Across-subject offline decoding of motor imagery from MEG and EEG

Long calibration time hinders the feasibility of brain-computer interfaces (BCI). If other subjects' data were used for training the classifier, BCI-based neurofeedback practice could start without the initial calibration. Here, we compare methods for inter-subject decoding of left- vs. right-hand motor imagery (MI) from MEG and EEG. Six methods were tested on data involving MEG and EEG measurements of healthy participants. Inter-subject decoders were trained on subjects showing good within-subject accuracy, and tested on all subjects, including poor performers. Three methods were based on Common Spatial Patterns (CSP), and three others on logistic regression with l1 - or l2,1 -norm regularization. The decoding accuracy was evaluated using (1) MI and (2) passive movements (PM) for training, separately for MEG and EEG. With MI training, the best accuracies across subjects (mean 70.6% for MEG, 67.7% for EEG) were obtained using multi-task learning (MTL) with logistic regression and l2,1-norm regularization. MEGyielded slightly better average accuracies than EEG. With PM training, none of the inter-subject methods yielded above chance level (58.7%) accuracy. In conclusion, MTL and training with other subject's MI is efficient for inter-subject decoding of MI. Passive movements of other subjects are likely suboptimal for training the MI classifiers.Peer reviewe