Good scientific practice in MEEG Research: Progress and Perspectives

Good Scientific Practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization.For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be periodically revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research.This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges.Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons

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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A novel integrating virtual reality approach for the assessment of the attachment behavioral system. Front. Psychol. 8, 1–7 (2017).Marín-Morales, J., Torrecilla, C., Guixeres, J. & Llinares, C. Methodological bases for a new platform for the measurement of human behaviour in virtual environments. DYNA 92, 34–38 (2017).Vince, J. Introduction to virtual reality. (Media, Springer Science & Business, 2004).Alcañiz, M., Baños, R., Botella, C. & Rey, B. The EMMA Project: Emotions as a Determinant of Presence. PsychNology J. 1, 141–150 (2003).Vecchiato, G. et al. Neurophysiological correlates of embodiment and motivational factors during the perception of virtual architectural environments. Cogn. Process. 16, 425–429 (2015).Slater, M. & Wilbur, S. A Framework for Immersive Virtual Environments (FIVE): Speculations on the Role of Presence in Virtual Environments. Presence Teleoperators Virtual Environ. 6, 603–616 (1997).Riva, G. et al. Affective Interactions Using Virtual Reality: The Link between Presence and Emotions. CyberPsychology Behav. 10, 45–56 (2007).Baños, R. M. et al Changing induced moods via virtual reality. In International Conference on Persuasive Technology (ed. Springer, Berlin, H.) 7–15, https://doi.org/10.1007/11755494_3 (2006).Baños, R. M. et al. Positive mood induction procedures for virtual environments designed for elderly people. Interact. Comput. 24, 131–138 (2012).Gorini, A. et al. Emotional Response to Virtual Reality Exposure across Different Cultures: The Role of the AttributionProcess. CyberPsychology Behav. 12, 699–705 (2009).Gorini, A., Capideville, C. S., De Leo, G., Mantovani, F. & Riva, G. The Role of Immersion and Narrative in Mediated Presence: The Virtual Hospital Experience. Cyberpsychology, Behav. Soc. Netw. 14, 99–105 (2011).Chirico, A. et al. Effectiveness of Immersive Videos in Inducing Awe: An Experimental Study. Sci. Rep. 7, 1–11 (2017).Blascovich, J. et al. 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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

Erratum de la mise au point - Résonance paramagnétique électronique. Historique et principaux résultats

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Polarisation dynamique du silicium 29 à basse température

Dynamic polarization of nuclei of silicon 29 in silicon has been studied at liquid helium temperature. Experimental results and apparatus are described.La polarisation dynamique des noyaux de silicium 29 dans le silicium a été étudiée à la température de l'hélium liquide. Les résultats obtenus avec un échantillon particulier sont décrits ainsi que les dispositifs expérimentaux utilisés

Complément bibliographique à la mise au point : Résonance paramagnétique électronique : historique et principaux résultats

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Étude des couplages entre spins nucléaires et spins électroniques dans certaines substances paramagnétiques présentant une forte anisotropie du facteur gyromagnétique

For certain paramagnetic salts, the g factor is small at an angle θ. By studying the nuclear relaxation time of the protons of such samples near this angle θ, we have shown the influence of processes where one electron spin flips against several nuclear spins.Certains sels paramagnétiques présentent pour un angle θ une valeur faible du facteur gyromagnétique électronique. L'étude, au voisinage de cet angle 0, du temps de relaxation nucléaire des protons contenus dans ces sels nous a conduits à mettre en évidence l'importance du processus où le renversement d'un spin électronique s'accompagne de celui de plusieurs spins nucléaires