Oneiris: An AI-augmented Brain-Computer Interface for Exploring Personal and Collective Dreamscapes
Bibliographic record
Abstract
Oneiris is an interactive, AI-augmented brain-computer interface installation that explores personal and collective dreamscapes through generative artificial intelligence, real-time electroencephalography (EEG) neurofeedback, and Indigenous symbolic systems. Participants wear a wireless EEG headset and contribute dream narratives and hand-drawn sketches on a digital tablet. These inputs are embedded using Contrastive Language–Image Pre-training (CLIP) and matched to ten Lakota dream symbols, displayed as a floating constellation within a 360° projection space. A diffusion-based AI pipeline simultaneously augments participants’ sketches and texts into continuously evolving “dreamscapes”, whose texture and color palette are modulated in real time by neural markers of hypnagogia and brain complexity. A Medicine Wheel–inspired interface—an Indigenous symbol embodying the cyclical nature of life—provides viewers with intuitive feedback about their cognitive state as they watch the visuals unfold. In parallel, an online companion platform archives dream contributions as nodes in a collective semantic map, enabling thematic clustering and public exploration. By striving to ethically integrate Indigenous epistemologies—particularly Lakota dream symbolism—into a neuroscientific and generative AI framework, Oneiris provides an innovative model for culturally sensitive, participatory art-science collaboration. The installation offers concrete methodologies for engaging with personal dreams as culturally embedded cognitive phenomena, creating spaces for introspection, collective storytelling, and cross-cultural dialogue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".