Add comprehensive BCI neural framework integration and web control panel

This commit implements a complete BCI integration and web control system:

## BCI Integration Framework
- Base adapter architecture for standardized BCI hardware interfaces
- OpenBCI adapter with BrainFlow support (Cyton, Ganglion, Daisy)
- Synthetic data generator for testing without hardware
- LSL (Lab Streaming Layer) integration for network streaming
- Circular buffers for efficient real-time data management
- Data quality metrics (SNR, artifact detection, quality scoring)

## Top 10 BCI Repository Integration Points
1. OpenBCI (OpenBCI_Python) - Direct hardware interface
2. BrainFlow - Universal board interface
3. MNE-Python - Signal processing pipeline
4. Lab Streaming Layer - Real-time streaming
5. PyRiemann - Riemannian geometry features
6. NeuroDSP - Oscillation analysis
7. NeuroKit2 - Multi-modal physiological signals
8. MOABB - BCI benchmarking framework
9. EEGNet - Deep learning models
10. Bcipy - Real-time experimentation

## Web Control Panel
- FastAPI backend with REST API endpoints
- WebSocket support for real-time data streaming
- CORS middleware for cross-origin requests
- System state management
- Configuration endpoints for HBCM and BCI setup
- Control commands (start, stop, pause, resume, reset)
- Data export (JSON, CSV formats)

## Visualization Components
- Real-time matplotlib plotting (6 subplots: time series + phase space)
- Interactive Plotly visualizations with zoom/pan
- 3D trajectory visualization (VTK/PyVista and Plotly 3D)
- Spectrogram analysis for frequency domain
- Base64 image encoding for web display
- Export to PNG, HTML, and interactive formats

## LaTeX Documentation Generator
- Automated report generation from simulation data
- Mathematical model descriptions (FitzHugh-Nagumo, Van der Pol)
- Configuration tables with parameters
- Statistical summaries (mean, std, min, max)
- BCI integration documentation
- BibTeX reference generation
- Automatic PDF compilation with pdflatex

## Bi-directional Data Flow Architecture
Hardware → Adapters → Buffers → HBCM → Control → Visualization
- <50ms latency target for real-time operation
- 1000+ samples/second multi-channel throughput
- Network synchronization via LSL timestamps
- Feedback loops for closed-loop BCI applications

## Examples and Documentation
- Comprehensive demo script showing all features
- 70+ page integration plan (docs/BCI_INTEGRATION_PLAN.md)
- Complete API reference in WEB_CONTROL_PANEL_README.md
- Installation guides for all dependencies
- Troubleshooting section
- Repository links to all integrated frameworks

## Dependencies Added
- FastAPI and uvicorn for web server
- WebSocket support
- BrainFlow and pylsl for BCI hardware
- MNE-Python for signal processing
- Plotly and PyVista for visualization
- LaTeX tools for documentation

All code includes comprehensive docstrings, type hints, and follows
the established Multi-Heart-Model patterns and conventions.

Author: claude

WEB_CONTROL_PANEL_README.md

@@ -0,0 +1,9 @@
+"""
+BCI Integration Package
+
+Provides adapters and streaming interfaces for Brain-Computer Interface hardware
+integration with the Multi-Heart-Model framework.
+"""
+
+__version__ = "1.0.0"
+__all__ = []

bci_integration/__init__.py

@@ -0,0 +1,23 @@
+"""BCI Data Acquisition Adapters."""
+
+from .bci_adapter_base import (
+    BCIAdapterBase,
+    BCIDataPacket,
+    BCIStreamConfig,
+    CircularBuffer,
+    DataQualityMetrics,
+    SignalType
+)
+from .openbci_adapter import OpenBCIAdapter
+from .synthetic_adapter import SyntheticAdapter
+
+__all__ = [
+    "BCIAdapterBase",
+    "BCIDataPacket",
+    "BCIStreamConfig",
+    "CircularBuffer",
+    "DataQualityMetrics",
+    "SignalType",
+    "OpenBCIAdapter",
+    "SyntheticAdapter",
+]