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<p> Although interest in brain-computer interfaces (BCIs) from researchers and consumers continues to increase, many BCIs lack the complexity and imaginative properties thought to guide users towards successful brain activity modulation. In this research, an experimental story brain-computer interface (ES-BCI) was developed, with which users could interact using cognitive strategies; specifically, thinking about the story and engaging with the main character of the story through their thought processes. In this system, the user’s frontal alpha asymmetry (FAA) measured with electroencephalography (EEG) was linearly mapped to the color saturation of the main character in the story. Therefore, the color saturation of the main character increased as FAA recorded from the participant’s brain activity increased above the FAA threshold required to receive visual feedback. A user-friendly experimental design was implemented using a comfortable EEG device and short neurofeedback (NF) training protocol. Eight distinct story scenes, each with a View and Engage NF component were created, and are referred to as blocks. In this system, seven out of 19 participants successfully increased FAA during the course of the study, for a total of ten successful blocks out of 152. Results concerning left (Lact) and right (Ract) prefrontal cortical activity contributions to FAA in both successful and unsuccessful blocks were examined to understand FAA measurements in greater detail. Additionally, electrodermal activity data (EDA) and self-reported questionnaire data were investigated to understand the user experience with this ES-BCI. Results suggest the potential of ES-BCI environments for engaging users and allowing for FAA modulation. New research directions for artistic BCIs investigating affect are discussed. </p>

Les affects, émotions et humeurs sont des processus complexes dont le fonctionnement précis échappe toujours à la neuroscience affective. Un récent mouvement des études IRMf s’est tourné vers la recherche d’effets aux niveaux inter- et intra-individuels en raison du manque d’applicabilité individuelle des résultats provenant de moyennes de groupes basées sur des données transversales. En particulier, la recherche intra-individuelle permet l’étude de liens directs entre l’affectivité et la connectivité chez de mêmes individus à travers le temps. De précédentes études en IRMf rapportent ce type associations chez un unique participant, notre objectif a été d’étudier les effets intra-individuels communs pour un groupe d’individus. Nous avons utilisé le jeu de données Day2day, composé de 40 à 50 sessions pour 6 participants, chaque session incluant des données d’IRMf au repos ainsi que d’auto-évaluations des états affectifs. Nous avons analysé la relation entre l’affectivité et la connectivité fonctionnelle entre des régions cérébrales précédemment liées aux émotions et affects à l’aide de régressions linéaires mixtes multivariées. Nos modèles ont isolé des patrons de connectivité communs et généralisables liés aux variations intra-individuelles de l’affectivité observées au cours de plusieurs semaines et mois. Ces modèles impliquaient particulièrement l’amygdale et l’insula. Nos résultats ouvrent la possibilité de reproduire de tels modèles sur des jeux de données plus larges ainsi qu’à évaluer l’hétérogénéité entre sujets au-delà des effets moyens. La caractérisation de tels processus neurobiologiques pourrait être d’une grande utilité en clinique comme biomarqueur transdiagnostique de l’état affectif ou potentielle cible thérapeutique. / Affects, emotions and moods are complex processes, the precise functioning of which still eludes affective neuroscience. A recent movement in fMRI has turned to research of effects at the inter- and intra-individual level in response to the lack of individual-level applicability of results from cross-sectional group mean studies. In particular, intra-individual research enables the study of direct links between affective states and underlying connectivity in individuals across time. Previous fMRI studies have described these associations in a single participant, our objective was to find shared intraindividual effects across multiple subjects. We have used the Day2day dataset, comprising 40 to 50 sessions for six participants, each session including data from resting-state fMRI scans and self-report measures of state affectivity. We have investigated the relationship between affectivity and connectivity in brain regions linked to emotions and affects using multivariate mixed linear analysis. Our models have isolated common and generalizable patterns of connectivity linked to variations in affectivity observed over multiple weeks and months. These models involved mainly the amygdala and insula. Our results incentivize the re-creation of such modelsin larger datasets, and to assess heterogeneity beyond group mean effects. The characterization of such neurobiological processes could be of great use in a clinical setting as a transdiagnostic biomarker or as a potential therapeutic target.