Sep 2026· Metaverse Science, Society and Law· 0 citations
TL;DR
This paper outlines a systematic framework designed to integrate generative AI modalities into the field of restorative tattoo art, specifically targeting psychological and somatic rehabilitation post-oncological disease.
Abstract
The rapid evolution of intelligent systems prompts a significant paradigm shift from purely computational optimization toward active mediation within highly subjective, delicate, and deeply personal human domains. Within the conceptual frameworks of Human-Centered Artificial Intelligence (HCAI) and Affective Computing, these intelligent architectures are increasingly recognized as dynamic digital intermediaries. This paper outlines a systematic framework designed to integrate generative AI modalities into the field of restorative tattoo art, specifically targeting psychological and somatic rehabilitation post-oncological disease. By combining multimodal large language models (LLMs) with advanced affective computing configurations, the proposed methodology resolves the "verbalization bottleneck" inherent in conventional trauma processing. The system maps uncodified emotional inputs into precise visual parameters, thereby facilitating post-traumatic growth, somatic reclamation, and clinical patient autonomy.
Psilocybin therapy in patients with cancer commonly produces vivid embodied and imaginal experiences that resist verbal description, yet most current integration approaches rely primarily on cognitive and verbal processing. We hypothesize that an approach to integration that directly engages this embodied register, without requiring translation into a verbal narrative, will enable patients to re-access salient aspects of their psychedelic experience and consolidate them into a durable, re-enterable touchstone. We call this mode of processing participatory-imaginal processing. Drawing on Embodied Imagination, predictive processing models, and clinical observations from psilocybin therapy, we describe how participatory-imaginal processing can be invoked during integration sessions, how it provides an opportunity to revisit somatic, affective, and imaginal material from the psychedelic experience, and the way it can function as a hypothesis-generating therapeutic framework. Our method is called the DREAM protocol (Drop In, Recount, Enter, Amplify, Merge), which we designed as a structured clinical heuristic that operationalizes our approach in a form that can be taught and evaluated for feasibility and fidelity. We hypothesize that using DREAM to activate a participatory-imaginal mode of processing in integration may help patients re-engage somatically salient aspects of their psychedelic experience and identify embodied touchstones that remain accessible long afterward. We illustrate the framework using clinical material from Bosnak’s work and spontaneous reports from participants in completed psilocybin trials. Two pilot studies currently in progress are designed to assess the feasibility, tolerability, and preliminary therapeutic relevance of this approach in the immediate post-psilocybin period.
Anthony L. Back, R. Bosnak· Frontiers in Psychiatry· 0 citations
SYNAPTICON is an artistic-research prototype at the brain–AI model interface, combining non-invasive brain–computer interfaces, foundation models, human–computer interaction, and live multimodal performance. Conceived as a “Brain Waves-to-Natural Language-to-Aesthetics” system, it translates EEG-derived neural activity into language outputs, which then operate as creative indices for immersive audiovisual scenes, storytelling, and altered perceptual experience. Building on this case study, we propose creative alignment as a framework for analyzing expressive neuro-AI systems across signal, semantic, narrative, and social layers. In this view, uncertainty is not merely a technical limitation, but a constitutive condition for co-agency, distributed authorship, and public interpretability. The project contributes a research-through-practice account of how brain–AI interfaces can become responsible artistic apparatuses for novel human expression, while foregrounding ethical questions around cognitive liberty, mental privacy, and the cultural legibility of model-mediated neural data.
Albert Barque-Duran, Ada Llaurado-Crespo, Jesús Vaquerizo-Serrano et al.· Proceedings of the Special I...· 0 citations
This article synthesizes contemporary research on the multifaceted impacts of piano interaction on human cognition, mental health, and neural processes, and explores its translation into intelligent human-computer systems. Evidence from clinical psychology demonstrates that structured piano training can significantly alleviate anxiety and depression in the elderly, enhance executive functions and working memory in aging populations, and serve as an effective component within multi-element interventions for severe mental illness, as exemplified by the GET UP PIANO trial. Neuroscientific investigations reveal that these benefits are supported by a specialized auditory-motor network, encompassing premotor and parietal cortices, which exhibits significant plasticity in experts and is engaged during both music perception and mental imagery. Unique patterns of musical processing in special populations, such as preserved fronto-temporal connectivity for song in autism spectrum disorder, provide a neurobiological rationale for music-based therapies. Critically, these findings are now informing the development of intelligent technologies. We explore how principles of cross-modal correspondence and personalization are being leveraged to create adaptive systems for health-tech, including closed-loop neurofeedback for neuromodulation, smart keyboards for motor rehabilitation using biofeedback, and AI-driven analysis of musical improvisation for mental health assessment. Finally, the review addresses key challenges, including the need for methodological standardization, mechanistic elucidation, and ethical HCI design focused on data security and long-term engagement. The convergence of neuroscience, clinical practice, and technology positions the piano as a powerful and evolving interface for enhancing human health and cognitive resilience.
SOMA is a multisensory robotic sculpture that engages bodily memory as a mode of human–AI communication. Visitors re-enact a remembered movement through a wearable sensing device, while the machine listens, interprets, and responds through its own mechanical choreography, forming an intimate, non-verbal dialogue between human and robotic body. At a time when human–AI interaction is dominated by text-based conversational agents, the work reclaims the body as a site of communication with artificial being, positioning somatic memory as both metaphor and method.
Pinyao Liu· Proceedings of the Special I...· 0 citations
Popular narratives about artificial intelligence (AI) and the afterlife provide a narrow vision of grief, memory, and technology. To foster a broader view of what AI might make possible in the context of death, we hosted a speculative design workshop with interdisciplinary experts. Through a series of design exercises and group discussions, participants were encouraged to develop novel ideas including memorialization and critically reflect on the opportunities and challenges. Our analysis identified four thematic areas: reimagining memorialization practices; grappling with ethical and practical concerns around data; reshaping continuing bonds with the dead; and confronting the commercial and infrastructural challenges of sustaining grief technologies. In our discussion, we argue that AI technologies offer openings for cultural critique and emotional insight. In broadening our vision for AI, this paper provides a more pluralistic and emotionally attuned vision for how AI might shape experiences of grief and memorialization.
Jed R. Brubaker, Tamara Borovica, Katrin Gerber et al.· Thanatos· 0 citations
Emotion recognition is a core component of affective computing, enabling intelligent systems to interpret human emotional states across critical applications such as healthcare, online education, and human–computer interaction. Early unimodal approaches relying solely on facial expressions, speech, or text have proven insufficient due to noise, cultural variability, and signal ambiguity, prompting a decisive shift toward multimodal integration. This systematic review, conducted following the PRISMA framework, examines this transition by analyzing 89 peer-reviewed studies selected from an initial pool of 160. The objective is to synthesize current methodologies, compare performance across modalities, and identify persistent technical and ethical barriers. Our findings reveal that multimodal systems, which fuse visual, acoustic, linguistic, and physiological signals, consistently outperform unimodal counterparts, achieving accuracy levels above 85% on benchmark datasets. Deep learning architectures particularly convolutional networks for spatial features, recurrent networks for temporal dependencies, and transformer-based models enhanced with attention mechanisms dominate the field, enabling effective dynamic weighting and fusion of heterogeneous data streams. Despite these advances, several challenges impede real-world deployment. Cross-subject and cross-session variability degrades generalizability, while data scarcity and the lack of large-scale, annotated multimodal corpora constrain model training. Computational complexity, especially in transformer-based fusion, limits edge-device feasibility, and ethical concerns surrounding privacy, demographic bias, and model interpretability remain unresolved. Future research must prioritize scalable and lightweight architectures, inclusive and culturally diverse dataset curation, and explainable AI frameworks that build user trust. Ultimately, transitioning these systems from laboratory prototypes to ethically sound, practical applications will require close interdisciplinary collaboration among computer scientists, psychologists, and ethicists, ensuring that emotion recognition technologies are not only accurate but also fair, transparent, and accessible across diverse real-world settings.
B. Bashir, Zayyanu Yunusa· American Journal of Artifici...· 0 citations