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EEGFlow: An Intelligent Adaptive Framework for Neural State Assessment

Project Overview

EEGFlow is an open-source framework that combines modern EEG technology with adaptive machine learning capabilities to create a universal platform for Ecological Momentary Assessment (EMA). The system uniquely features an AI-driven architecture that automatically extends its capabilities to support new models and devices, making it truly future-proof and universally accessible. Although we will start with simply laying the foundation for LLMs to extend the architecture, in the early stages we can add it ourselves and focus on building the framework and application.

Core Technologies

  • Frontend: React.js, TypeScript, Tailwind CSS
  • Backend: Python, PyTorch, TensorFlow
  • AI Integration: Large Language Model for automatic adaptation
  • Data Processing: Real-time EEG signal processing
  • Visualization: Recharts, Interactive dashboards

Key Features

1. Intelligent Model Integration

  • Auto-Adaptive Model Support
    • LLM-powered automatic integration of new model types
    • Self-extending registry system
    • Dynamic code generation for new frameworks
    • Automated validation and testing

2. Universal Device Compatibility

  • Support for major EEG devices:
    • Neurosity
    • Emotiv
    • Muse
    • OpenBCI
    • Custom device integration capability

3. Real-Time Processing Pipeline

  • Configurable preprocessing stages
  • Adaptive sampling rates
  • Real-time state classification
  • Customizable event triggers

4. Researcher-Friendly Interface

  • Intuitive study setup wizard
  • Real-time monitoring dashboard
  • Interactive visualization tools
  • Automated data collection and analysis

Technical Architecture

Frontend Layer

- Dashboard Component
- Device Management
- Model Configuration
- Real-time Visualization
- Study Configuration

Backend Layer

- Model Registry System
- LLM Integration Service
- Data Processing Pipeline
- Device Interface Layer

AI Adaptation Layer

class ModelRegistry:
    - Dynamic Adapter Generation
    - Automatic Framework Support
    - Code Validation
    - Version Control

Use Cases

1. Academic Research

  • Cognitive state monitoring
  • Attention studies
  • Emotion recognition
  • Learning assessment

2. Clinical Applications

  • Mental state monitoring
  • Treatment response tracking
  • Biofeedback systems
  • Intervention timing

3. Development and Integration

  • Custom model deployment
  • New device integration
  • Framework adaptation
  • Protocol development

Getting Started

For Researchers

# Install EEGFlow
pip install eegflow

# Initialize project
eegflow init my-study

# Configure study parameters
eegflow config --study-type=attention

For Developers

# Add new model support
async def integrate_new_model(config_path: str):
    registry = ModelRegistry()
    await registry.add_new_model_support(
        config=load_config(config_path),
        llm_client=LLMClient()
    )

Unique Advantages

1. AI-Powered Adaptability

  • Automatic support for new model architectures
  • Self-extending capabilities
  • Intelligent error handling
  • Continuous learning system

2. Universal Compatibility

  • Framework-agnostic design
  • Device-independent architecture
  • Extensible preprocessing pipeline
  • Flexible data formats

3. Research-Oriented Features

  • Custom sampling intervals
  • State trajectory analysis
  • Event-triggered assessment
  • Data export capabilities

Development Roadmap

Phase 1: Core Infrastructure

  • Basic framework implementation
  • Essential device support
  • UI/UX development
  • Data processing pipeline

Phase 2: AI Integration

  • LLM integration
  • Automatic adapter generation
  • Code validation system
  • Framework detection

Phase 3: Advanced Features

  • Complex study designs
  • Advanced visualizations
  • Multi-device support
  • Advanced analytics

Contributing

The project welcomes contributions in:

  • Device integration
  • Model adaptation
  • UI/UX improvements
  • Documentation
  • Testing and validation

Impact

EEGFlow aims to:

  • Democratize EMA research
  • Accelerate neuroscience studies
  • Enable complex study designs
  • Foster collaboration
  • Reduce technical barriers

Future Vision

  • Cloud integration
  • Mobile applications
  • Real-time collaboration
  • Advanced AI capabilities
  • Expanded device support