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`