48 research outputs found
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 ElectroEncephaloGraphyâ
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 participantsâ 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 participantsâ performance with a mean error of less than 3
points. This study determined how usersâ 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 timeâfrequency 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
Nicotine deprivation elevates neural representation of smoking-related cues in object-sensitive visual cortex: a proof of concept study
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).MarĂn-Morales, J.; Higuera-Trujillo, JL.; Greco, A.; Guixeres Provinciale, J.; Llinares MillĂĄn, 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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Investigation of the Physiological Differences between Immersive Virtual Environment and Indoor Enviorment in a Building. Indoor adn Built Enviornment 0, Accept (2017).Combrisson, E. & Jerbi, K. Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. J. Neurosci. Methods 250, 126â136 (2015).He, C., Yao, Y. & Ye, X. An Emotion Recognition System Based on Physiological Signals Obtained by Wearable Sensors. In Wearable Sensors and Robots: Proceedings of International Conference on Wearable Sensors and Robots 2015 (eds. Yang, C., Virk, G. S. & Yang, H.) 15â25, https://doi.org/10.1007/978-981-10-2404-7_2 (Springer Singapore, 2017)
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 alignĂ©s (VACNT) sont des matĂ©riaux aux propriĂ©tĂ©s structurales, Ă©lectriques et thermiques trĂšs intĂ©ressantes pour de nombreuses applications. La mĂ©thode de choix pour la synthĂšse de VACNT de haute qualitĂ© est le dĂ©pĂŽt chimique en phase vapeur catalytique (CCVD). Cette Ă©tude porte sur la croissance de tapis de VACNT sur des substrats d'intĂ©rĂȘt par CCVD d'aĂ©rosols Ă basse tempĂ©rature. Cette mĂ©thode, dĂ©veloppĂ©e au sein du laboratoire Edifices NanomĂ©triques (LEDNA) consiste Ă injecter simultanĂ©ment dans le rĂ©acteur un prĂ©curseur catalytique et un prĂ©curseur carbonĂ© de maniĂšre Ă gĂ©nĂ©rer in-situ la formation des particules catalytiques Ă l'origine de la croissance des VACNT. Cette mĂ©thode est un procĂ©dĂ© de synthĂšse en continu, en une seule Ă©tape, simple, peu coĂ»teux et transposable Ă grande Ă©chelle. Elle a Ă©tĂ© jusqu'alors dĂ©veloppĂ©e surtout Ă haute tempĂ©rature (800 Ă 850°C) [1-3] et rĂ©cemment elle a Ă©tĂ© ajustĂ©e Ă la croissance sur aluminium, pour la fabrication d'Ă©lectrodes de supercondensateurs, qui imposait des tempĂ©ratures plus basses de l'ordre de 600°C [4,5]. En se basant sur des travaux antĂ©rieurs, notre procĂ©dĂ© a Ă©tĂ© modifiĂ© par l'adjonction d'hydrogĂšne en phase gazeuse pour favoriser la dĂ©composition du prĂ©curseur catalytique (ferrocĂšne) Ă basse tempĂ©rature [2], et par le remplacement du prĂ©curseur carbonĂ© liquide (toluĂšne) par l'acĂ©tylĂšne, facile Ă dĂ©composer Ă basse tempĂ©rature [6]. Les rĂ©sultats rĂ©cents mettent en Ă©vidence une croissance de nanotubes alignĂ©s et denses Ă ces faibles tempĂ©ratures avec des vitesses de croissance qui sont comparables Ă celles obtenues dans l'Ă©tat de l'art pour des mĂ©thodes en deux Ă©tapes et assistĂ©es (plasma [7] ou filaments chauds [8]). Toutefois, une diminution de la vitesse de croissance en fonction de la durĂ©e de synthĂšse se traduisant par une limitation de la hauteur des tapis de VACNT a Ă©tĂ© observĂ©e [9,10]. Dans ce contexte, l'objectif principal est d'approfondir notre comprĂ©hension de la croissance des VACNT spĂ©cifiquement Ă basse tempĂ©rature et d'identifier les mĂ©canismes mis en jeu. L'enjeu Ă©tant un meilleur contrĂŽle du procĂ©dĂ© de croissance dans ces conditions oĂč les phĂ©nomĂšnes physico-chimiques Ă l'oeuvre peuvent ĂȘtre modifiĂ©s 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