omnineuro-bci-framework

name	omnineuro-bci-framework
description	OmniNeuro multimodal HCI framework for explainable BCI feedback — integrates Physics (Energy), Chaos (Fractal Complexity), and Quantum-Inspired uncertainty modeling to transform BCI from silent decoder to transparent feedback partner. Use when designing brain-computer interfaces with interpretability, neurofeedback systems, BCI sonification, multimodal BCI feedback, or quantum-inspired uncertainty in neural decoding (arXiv: 2601.00843)
license	Complete terms in LICENSE.txt
metadata	{"arxiv_id":"2601.00843","published":"2026-01-28","authors":"Ayda Aghaei Nia","tags":["bci","neurofeedback","interpretability","quantum-inspired","chaos-theory","sonification","hci","neural-decoding"]}

OmniNeuro BCI Framework

Multimodal HCI framework for explainable brain-computer interface feedback that transforms the BCI from a silent decoder into a transparent feedback partner.

Problem

Deep Learning improved BCI decoding accuracy but clinical adoption is hindered by "Black Box" algorithms, leading to:

• User frustration from opaque decision-making
• Poor neuroplasticity outcomes due to lack of understanding
• Limited trust in clinical settings

Solution: Three Interpretability Engines

1. Physics (Energy) Engine

• Maps neural signal energy patterns to interpretable visual/audio feedback
• Uses energy-based analysis to show users "how much" neural activity is present
• Provides intuitive magnitude feedback for motor imagery, attention, or cognitive load tasks

2. Chaos (Fractal Complexity) Engine

• Computes fractal dimensions and entropy measures of neural signals
• Reveals the "complexity" of brain states beyond simple amplitude
• Useful for detecting transitions between cognitive states, sleep stages, or attentional focus
• Metrics: Higuchi fractal dimension, sample entropy, Lyapunov exponents

3. Quantum-Inspired Uncertainty Engine

• Models decoding uncertainty using quantum probability-inspired frameworks
• Provides probabilistic rather than deterministic feedback
• Shows users the confidence level of BCI predictions
• Enables users to understand when the system is uncertain vs. confident

Feedback Modalities

Visual Feedback

• Real-time visualization of neural energy, complexity, and uncertainty
• Color-coded confidence indicators
• Temporal evolution of brain state patterns

Sonification

• Audio mapping of neural dynamics for eyes-free operation
• Pitch/frequency encoding of signal amplitude
• Rhythmic patterns reflecting neural complexity
• Timbre variations representing decoding uncertainty

Generative AI Integration

• AI-generated explanations of BCI decisions in natural language
• Context-aware feedback based on user's task and history
• Personalized feedback adaptation over time

Methodology

Pipeline Architecture

1. Signal Acquisition: EEG/MEG/ECoG neural signals
2. Feature Extraction: Time-domain, frequency-domain, and nonlinear features
3. Three-Engine Analysis:
   • Energy computation from signal power
   • Fractal complexity analysis
   • Quantum-inspired uncertainty estimation
4. Feedback Generation:
   • Visual rendering
   • Audio synthesis (sonification)
   • Natural language explanation
5. User Interaction Loop: Real-time closed-loop adaptation

Clinical Applications

• Stroke Rehabilitation: Transparent feedback for motor imagery training
• BCI Gaming: Engaging multimodal feedback for neurofeedback games
• Cognitive Assessment: Complexity-based assessment of cognitive states
• Neuroplasticity Training: Enhanced learning through interpretable feedback

Activation Keywords

bci, neurofeedback, brain-computer interface, interpretability, explainable ai, sonification, quantum-inspired, chaos theory, fractal, energy analysis, neural decoding, hci, multimodal feedback, clinical bci, motor imagery, neuroplasticity

Cross-Domain Connections

• Quantum Computing: Quantum probability-inspired uncertainty modeling for neural decoding
• Chaos Theory: Fractal analysis of neural signals for complexity-based feedback
• Neuroscience: Neuroplasticity outcomes enhanced through interpretable feedback
• HCI: Multimodal user experience design for clinical BCI systems
• AI: Generative AI for natural language explanation of BCI decisions

References

• arXiv:2601.00843 — OmniNeuro: A Multimodal HCI Framework for Explainable BCI Feedback via Generative AI and Sonification
• Related skills: bci-adversarial-robustness, eeg-foundation-model-adapters, quantum-probability-statistics