Neurazum

Neurazum is an innovation startup that aims to analyze human thoughts and emotions as well as diagnoses of diseases and disorders using artificial intelligence by analyzing EEG signals. This initiative aims to facilitate the detection of diseases and disorders by precisely monitoring brain activity. In addition, our image processing technologies can also diagnose diseases such as Alzheimer's, Parkinson's, MS, ALS from brain images.

Neurazum is an exciting technological step that has the potential to revolutionize many sectors such as medicine, neuroscience, communication and education.

Featured Models and Systems

bai Family — EEG-Intelligent Models

A modular family of brain–AI architectures built for real-time EEG interpretation, cognitive state analysis, and neural signal intelligence.
Each model is task-specific, lightweight, and optimized for adaptive Brain–Computer Interface (BCI) use.

• bai-{Task}-{Channels} (e.g. bai-Epilepsy-6, bai-Emotion-8, bai-Mind-4)
  The naming system reflects both the EEG channel count and the intended task.
  Each model is trained for a single, well-defined function such as seizure detection, emotion classification, or EEG-to-text reasoning.
  This modular design allows models to be used independently or combined into larger multimodal systems.

• bai-{Task}-{Channels}-v{x} (e.g. bai-Epilepsy-6-v1, bai-Emotion-8-v2)
  Version numbers indicate progressive refinements in accuracy, data diversity, and real-time stability.
  Each major version (v1, v2, v3, …) represents a full upgrade with new datasets, improved architectures, or enhanced task performance.
  Minor updates are integrated into the next major version rather than numbered separately.

• bai-{Task}-{Channels}P-v{x} (e.g. bai-Epilepsy-6P-v1, bai-Emotion-8P-v2)
  The Professional series, trained on medical-grade EEG datasets and validated for clinical or research deployment.
  These models adhere to strict signal fidelity, diagnostic reliability, and interpretability standards for regulated environments.

Vbai Family — Imaging and Brain Models

A collection of open-access models for neurological imaging, analysis, and 3D brain modeling.

• Vbai-{x.y} (e.g. Vbai-2.5, Vbai-2.6, Vbai-3.0)
  Fully open-source models designed to handle multi-domain biomedical tasks.
  The major version coefficient (e.g. 2.0 → 3.0) changes with the introduction of new core functionalities.
  Example: Vbai-2.5 supports simultaneous tumor and dementia detection, enabling complex dual diagnostic workflows.

• Vbai-3D-{x.y} (e.g. Vbai-3D-1.0, Vbai-3D-2.1)
  Specialized 3D-capable open models for volumetric brain data analysis.
  Each version (1.0, 2.0, etc.) represents incremental improvements in precision, processing speed, and multimodal fusion.
  These models can directly process 3D brain MRI inputs to detect structural and degenerative anomalies in real time.

Tbai Family — Compact Neuro-Linguistic Models

A family of lightweight transformer-based models designed to bridge natural language and neural data interpretation.
Tbai models function similarly to small-scale tokenizers like T5, optimized for concise reasoning and domain-specific understanding.

• Tbai-HF-{x} (e.g. Tbai-HF-1, Tbai-HF-2)
  Models trained for neuroimaging-based interpretation.
  Capable of generating analytical or descriptive text directly from brain imaging data such as MRI or CT scans.
  Each version enhances domain adaptation, linguistic fluency, and cross-modal comprehension between visual brain patterns and textual insight.

• Tbai-NAI-{x} (e.g. Tbai-NAI-1, Tbai-NAI-2)
  Models specialized for EEG-based cognitive commentary.
  Designed to interpret brainwave signals and translate them into meaningful linguistic explanations.
  Subsequent versions expand accuracy in recognizing temporal EEG events, emotional states, and neurological anomalies.

Lbai Family — Large-Scale Cognitive Reasoning Models

A suite of large language models (LLMs) trained on medical, neuroscientific, and cognitive corpora to perform advanced reasoning and diagnostic dialogue.

• Lbai-{x}-{state} (e.g. Lbai-1-preview, Lbai-2-base, Lbai-1-it)
  These models emulate expert-level reasoning, trained to think and respond like medical professionals, with a strong focus on neuroscience, neurophysiology, and clinical linguistics.
  Lbai models aim to achieve near-complete domain comprehension across brain science, medicine, and cognitive AI research.

Summary

The bai, Vbai, Tbai, and Lbai families form a cohesive ecosystem within the NeurAI and HealFuture architecture.

• bai models specialize in EEG signal intelligence, handling real-time neural data for cognitive and diagnostic applications.
• Vbai models extend this capability into neuroimaging, providing open-source frameworks for 2D and 3D brain analysis.
• Tbai models serve as the linguistic interpreters, translating neural activity and brain imagery into natural language insights.
• Lbai models deliver deep reasoning and expert cognition, acting as medical-grade large language models trained for neuroscience and diagnostics.

Together, they define a unified hierarchy of neural understanding, where data from brain signals and imaging seamlessly integrates with language and reasoning.
This layered system enables scalable, interpretable, and clinically meaningful intelligence across all domains of brain–AI interaction.

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