MCP Integration¶

This document explains how the Brain-Computer Interface (BCI) integrates with the Model Context Protocol (MCP) in the BCI-MCP system.

Overview¶

The Model Context Protocol (MCP) allows large language models (LLMs) to receive and process brain activity data as additional context. This integration enables AI systems to respond to and adapt based on neural signals, creating a more intuitive human-AI interaction.

Key Integration Points¶

Data Flow from BCI to MCP¶

BCI Device → Signal Processing → Feature Extraction → Context Formatting → MCP API → Language Model

BCI Device: Acquires raw neural signals
Signal Processing: Filters and cleans the signals
Feature Extraction: Extracts meaningful features from processed data
Context Formatting: Converts features into MCP-compatible format
MCP API: Receives the formatted context
Language Model: Uses the context for enhanced responses

Context Data Format¶

BCI data is structured into the MCP context format as follows:

{
  "bci_data": {
    "metadata": {
      "device_type": "openBCI",
      "channels": 8,
      "sampling_rate": 250,
      "timestamp": 1679569835.245
    },
    "features": {
      "band_powers": {
        "alpha": [0.75, 0.65, 0.82, 0.71, 0.68, 0.72, 0.69, 0.77],
        "beta": [0.45, 0.52, 0.48, 0.51, 0.47, 0.49, 0.50, 0.46],
        "theta": [0.62, 0.58, 0.63, 0.59, 0.61, 0.60, 0.57, 0.64],
        "delta": [0.85, 0.88, 0.83, 0.87, 0.86, 0.84, 0.89, 0.82]
      },
      "connectivity": {
        "coherence": [[0.8, 0.5, 0.3], [0.5, 0.7, 0.4], [0.3, 0.4, 0.9]]
      },
      "statistics": {
        "mean": [0.12, 0.15, 0.11, 0.14, 0.13, 0.12, 0.16, 0.11],
        "variance": [0.05, 0.06, 0.04, 0.05, 0.05, 0.04, 0.06, 0.04]
      },
      "events": [
        {"type": "blink", "timestamp": 1679569835.125, "confidence": 0.92},
        {"type": "attention_spike", "timestamp": 1679569836.352, "confidence": 0.87}
      ]
    },
    "cognitive_state": {
      "attention": 0.75,
      "relaxation": 0.62,
      "cognitive_load": 0.45,
      "arousal": 0.58
    }
  }
}

Feature Extraction¶

The BCI-MCP system extracts various features from neural signals for use with MCP:

Spectral Features¶

Band Powers: Power in different frequency bands (alpha, beta, theta, delta, gamma)
Spectral Entropy: Measure of unpredictability in the frequency domain
Peak Frequencies: Dominant frequencies in the signal

Temporal Features¶

Event-Related Potentials: Neural responses to specific events
Statistical Measures: Mean, variance, skewness, kurtosis
Signal Complexity: Measures like Hjorth parameters and sample entropy

Spatial Features¶

Spatial Filters: Common Spatial Patterns (CSP) and similar techniques
Connectivity Measures: Coherence, phase synchrony between channels
Source Localization: Estimates of neural source activity

Cognitive State Estimation¶

The system estimates cognitive states from BCI data for enhanced interaction:

Attention Level: Focus and engagement
Cognitive Load: Mental workload
Emotional State: Valence and arousal
Relaxation: Calm and meditative states

Integration Methods¶

Direct API Integration¶

from bci_mcp.devices import OpenBciDevice
from bci_mcp.processing import FeatureExtractor
from bci_mcp.mcp import McpClient

# Initialize BCI device
device = OpenBciDevice(port="/dev/ttyUSB0")
device.connect()
device.start_stream()

# Extract features from BCI data
extractor = FeatureExtractor()
bci_data = device.get_data(seconds=5)
features = extractor.process(bci_data)

# Create MCP client and send query with BCI context
client = McpClient(api_key="your_api_key")
response = client.query(
    prompt="How should I modify my meditation practice based on my current state?",
    context={"bci_data": features}
)

print(response.text)

# Clean up
device.stop_stream()
device.disconnect()

Streaming Integration¶

For real-time applications, BCI-MCP supports streaming integration:

from bci_mcp.devices import OpenBciDevice
from bci_mcp.processing import StreamProcessor
from bci_mcp.mcp import McpClient

# Initialize components
device = OpenBciDevice(port="/dev/ttyUSB0")
processor = StreamProcessor()
client = McpClient(api_key="your_api_key")

# Configure streaming callback
def on_features_extracted(features):
    response = client.query(
        prompt="Adapt to my current cognitive state",
        context={"bci_data": features}
    )
    print(response.text)

# Start streaming with processing
device.connect()
processor.set_callback(on_features_extracted)
processor.process_stream(device)

# Run for 60 seconds then clean up
import time
time.sleep(60)
processor.stop()
device.disconnect()

Applications¶

Adaptive Interfaces¶

BCI-MCP enables interfaces that adapt to the user's cognitive state:

Adjusting complexity based on cognitive load
Providing more assistance when attention decreases
Adapting information presentation based on emotional state

Neuro-Augmented AI¶

Combining neural data with AI capabilities:

Enhanced creative processes using neural feedback
Personalized learning systems that adapt to cognitive state
Meditation and mindfulness applications with neural guidance

Assistive Technology¶

BCI-MCP provides powerful assistive capabilities:

Communication systems for individuals with motor disabilities
Cognitive assistance that adapts to the user's needs
Emotional support systems with neural monitoring

Best Practices¶

Privacy and Security¶

When integrating BCI with MCP, consider these privacy practices:

Implement data minimization by only sending necessary features
Use secure, encrypted connections for all data transmission
Provide clear user control over when and what data is sent
Consider local processing when possible

Performance Optimization¶

For optimal performance:

Balance feature richness with transmission efficiency
Implement caching for frequently used context
Use incremental updates for streaming applications
Monitor latency and adjust processing accordingly

Next Steps¶

For more detailed information on signal processing techniques, see the Advanced Signal Processing documentation.