An open-source artificial intelligence framework designed for collecting, parsing, and decoding anomalous information received by subjects during Altered States of Consciousness (ASC) — including ordinary dreams, lucid dreams, out-of-body experiences (OBE), and near-death experiences (NDE).
Instead of tracking human-to-human social similarities, DreamCode isolates collective information patterns from individual psychological noise, serving as an empirical detector for non-local, precognitive, and Unidentified Anomalous Phenomena (UAP) related signals.
The primary research question driving this project is:
Can humanity or individual humans unconsciously, through extrasensory perception, explore other worlds, communicate with other minds, and foresee the future, translating the information received into their own interpretations?
Rooted in over a decade of systematic observation following a childhood near-death experience, this project operates on the hypothesis that human dreams and ASCs act as highly sensitive neurocognitive windows.
External non-local signals, which are fundamentally emotional or multi-dimensional, are rarely received by the brain in a literal, descriptive format. Instead, the cognitive matrix translates these signals into deeply individualized, symbolic associative imagery based on the perceiver's unique memory architecture.
By analyzing large datasets of subjective narratives across their underlying semantic, emotional, and archetypal vectors, DreamCode maps structural commonalities emerging simultaneously among independent, unconnected percipients — revealing true objective signals historically dismissed as subjective "dream mythology."
DreamCode utilizes a three-phase methodology combining qualitative phenomenology with unsupervised computational data science.
- Decentralized Web Application: A lightweight web interface allowing global users to log detailed descriptions of anomalous experiences anonymously.
- Rigid Classification: Inputs are segmented by state (Ordinary Dream, Lucid Dream, OBE, NDE) and calibrated against a standardized 3-point scale measuring emotional valence and subjective intensity.
- Granular Sentence Segmentation: Narratives are parsed at the scene/sentence level to isolate distinct events.
- Dense Vectorization: Extracted text fragments are transformed into dense vector spaces using all-MiniLM-L6-v2 SentenceTransformer.
- Blind Archetype Detection using KMeans algorithm.
- Dimensionality Reduction with t-SNE to construct Semantic Pattern Map.
- Representative Motif Synthesis for each cluster.
The fully operational analytical engine is embedded directly on the project's website:
🔗 https://www.masterogon.art/index.php/dreamcode/
The application is entirely free, decentralized, and anonymous.
Kandyba, P. (2024). Extrasensory perception in dreams through emotions and symbols.
International Journal of Dream Research, 17(1).
https://doi.org/10.11588/ijodr.2024.1.102315
Keywords
#Consciousness #AnomalousCognition #UAP #UFO #LucidDreaming #OBE #NDE #MachineLearning #DreamCode