Process Flowcharts

Process Steps

Key Process Steps:

1. Discovery Phase

   • USB devices detected via serial port enumeration
   • BLE devices found through active scanning
   • WiFi/LSL devices discovered via mDNS broadcast

2. Connection Setup

   • Driver verification and installation if needed
   • Protocol-specific handshake (varies by device type)
   • Connection parameters negotiation

3. Configuration

   • Sample rate selection (250Hz, 500Hz, 1000Hz)
   • Channel mapping and activation
   • Digital filter configuration (notch, bandpass)

4. Quality Assurance

   • Optional impedance checking per channel
   • Signal quality validation
   • Automatic bad channel detection

5. Data Streaming

   • Zero-copy ring buffer initialization
   • Real-time data validation
   • Asynchronous Pub/Sub publishing

Error Handling

Error Handling Mechanisms:

• Connection Failures: Exponential backoff retry strategy
• Driver Issues: Automatic driver download and installation
• Protocol Errors: Fallback to compatible protocol versions
• Data Validation: Packet checksum and timestamp verification
• Network Issues: Automatic reconnection with state preservation
• Buffer Overflow: Backpressure handling and flow control

Recovery Strategies:

# Retry configuration
MAX_RETRIES = 3
INITIAL_BACKOFF = 1.0  # seconds
MAX_BACKOFF = 30.0     # seconds
BACKOFF_MULTIPLIER = 2.0

Implementation Details

Implementation References:

• Device Discovery: neural-engine/src/devices/discovery_service.py
• Connection Manager: neural-engine/src/devices/device_manager.py
• Protocol Handlers: neural-engine/src/devices/protocols/
• Ring Buffer: neural-engine/src/core/ring_buffer.py
• Pub/Sub Client: neural-engine/src/messaging/pubsub_client.py

Key Classes:

class DeviceDiscoveryService:
    async def discover_devices(self) -> List[DeviceInfo]
 
class DeviceManager:
    async def connect(self, device_id: str) -> Device
    async def configure(self, device: Device, config: DeviceConfig)
    async def start_streaming(self, device: Device)

Session Lifecycle

Session Management Steps:

1. Session Creation

   • Generate unique session identifier (UUID v4)
   • Associate with patient/participant record
   • Set recording permissions and access controls

2. Device Selection

   • Verify device availability and compatibility
   • Check for device conflicts or exclusive access
   • Reserve devices for session duration

3. Configuration

   • Recording duration (continuous or fixed)
   • Event triggers (manual, scheduled, or signal-based)
   • Storage options (hot, warm, cold tiers)

4. Recording Process

   • Synchronized multi-device recording
   • Real-time health monitoring
   • Pause/resume functionality

5. Session Completion

   • Data file finalization and integrity checks
   • Metadata generation with session summary
   • Archival to appropriate storage tier

Data Management

Data Management During Session:

class SessionDataManager:
    def __init__(self, session_id: str):
        self.session_id = session_id
        self.storage_tier = "hot"  # bigtable
        self.buffer_size = 1_000_000  # samples
 
    async def write_data(self, data: np.ndarray):
        # Write to hot storage with buffering
        await self.hot_storage.write_batch(data)
 
    async def finalize(self):
        # Move to appropriate tier based on config
        if self.long_term_storage:
            await self.migrate_to_cold_storage()

Storage Hierarchy:

• Hot: Real-time access during recording
• Warm: Recent sessions (< 30 days)
• Cold: Long-term archive (> 30 days)

Error Recovery

Error Recovery Procedures:

Cleanup Operations:

• Release device reservations
• Flush all buffers to storage
• Update session status in database
• Notify connected clients
• Generate audit trail entry

Pipeline Stages

Data Processing Pipeline:

1. Acquisition Stage

   • Receive raw data from device drivers
   • Apply hardware timestamps with microsecond precision
   • Validate packet integrity and sequence

2. Pre-Processing Stage

   • Remove DC offset using high-pass filter (0.1 Hz)
   • Apply notch filter for powerline noise (50/60 Hz)
   • Optional: Apply bandpass filter based on use case

3. Quality Assessment

   • Check for saturated channels
   • Detect disconnected electrodes
   • Calculate signal-to-noise ratio
   • Mark poor quality segments

4. Buffering Strategy

   • Ring buffer: 10,000 samples per channel
   • Batch threshold: 1,000 samples (4 seconds @ 250Hz)
   • Emergency flush: Every 100ms

5. Distribution

   • Parallel fan-out to multiple consumers
   • Priority-based message delivery
   • Guaranteed delivery to storage

Performance Metrics

Performance Characteristics:

Latency Breakdown:

Device → Buffer:       0.1ms
Buffer → Processing:   2.0ms
Processing → Pub/Sub:  5.0ms
Pub/Sub → WebSocket:   3.0ms
WebSocket → Client:    5.0ms
-------------------------------
Total End-to-End:     15.1ms

Scalability Limits:

• Single node: 64 channels @ 1kHz
• Distributed: 1000+ channels @ 1kHz
• WebSocket connections: 10,000 concurrent

Optimization

Optimization Techniques:

# Vectorized processing using NumPy
class OptimizedProcessor:
    def __init__(self):
        # Pre-compute filter coefficients
        self.notch_b, self.notch_a = signal.iirnotch(
            60.0, 30.0, fs=1000
        )
 
    def process_batch(self, data: np.ndarray) -> np.ndarray:
        # Process entire batch at once
        # Shape: (n_channels, n_samples)
 
        # Remove DC offset (vectorized)
        data = data - np.mean(data, axis=1, keepdims=True)
 
        # Apply notch filter (parallelized)
        data = signal.filtfilt(
            self.notch_b, self.notch_a, data, axis=1
        )
 
        return data

Memory Optimization:

• Zero-copy operations where possible
• Memory pool for buffer allocation
• Efficient data structure alignment

Network Optimization:

• Binary protocol (MessagePack)
• Compression for > 1000 samples
• Batched WebSocket frames

Training Pipeline

Model Training Pipeline:

Supported Model Types:

MODEL_ARCHITECTURES = {
    "classification": {
        "EEGNet": "Compact CNN for EEG",
        "DeepConvNet": "Deep CNN architecture",
        "ShallowConvNet": "Shallow CNN for online",
        "LSTM-FCN": "LSTM + Fully Convolutional",
        "Transformer": "Attention-based model"
    },
    "regression": {
        "CNN-LSTM": "Feature prediction",
        "TCN": "Temporal Convolutional Network",
        "WaveNet": "Dilated convolutions"
    },
    "anomaly_detection": {
        "Autoencoder": "Reconstruction-based",
        "VAE": "Variational Autoencoder",
        "GAN": "Generative Adversarial Network"
    }
}

Deployment Strategy

Deployment Process:

stages:
  - name: Model Validation
    steps:
      - Unit tests for model code
      - Integration tests with pipeline
      - Performance benchmarks
      - Security scanning
 
  - name: Containerization
    steps:
      - Build Docker image
      - Optimize for inference (quantization)
      - Create ONNX export
      - Push to Artifact Registry
 
  - name: Staging Deployment
    steps:
      - Deploy to staging cluster
      - Run smoke tests
      - Validate against test data
      - Monitor for 24 hours
 
  - name: Canary Release
    steps:
      - Deploy to 5% of traffic
      - Monitor key metrics
      - Compare with baseline
      - Gradual rollout to 25%, 50%, 100%
 
  - name: Production Monitoring
    steps:
      - Real-time performance tracking
      - Error rate monitoring
      - Resource utilization
      - User feedback collection

Rollback Strategy:

• Automatic rollback on error rate > 5%
• Manual rollback capability
• Previous 3 versions kept hot
• Zero-downtime rollback

Monitoring & Retraining

Model Monitoring Metrics:

Retraining Triggers:

class RetrainingTrigger:
    def __init__(self):
        self.triggers = {
            "scheduled": CronTrigger("0 0 * * 0"),  # Weekly
            "performance": ThresholdTrigger(
                metric="accuracy",
                threshold=0.85,
                direction="below"
            ),
            "drift": DriftTrigger(
                method="kolmogorov_smirnov",
                threshold=0.05
            ),
            "data_volume": VolumeTrigger(
                new_samples=10000,
                min_interval_days=7
            ),
            "clinical_feedback": ManualTrigger(
                approval_required=True
            )
        }
 
    async def check_triggers(self):
        for name, trigger in self.triggers.items():
            if await trigger.should_fire():
                await self.initiate_retraining(name)

Continuous Learning Pipeline:

• Incremental learning for online adaptation
• Active learning for efficient labeling
• Transfer learning from pre-trained models
• Federated learning for privacy-preserving updates