Platform Features

NeuraScale provides a comprehensive suite of features designed for researchers, clinicians, and developers working with brain-computer interfaces. Our platform combines cutting-edge neural signal processing with enterprise-grade infrastructure.

Feature Categories

Device Integration

Universal support for 30+ BCI devices with plug-and-play connectivity

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Data Processing

Real-time signal processing with sub-100ms latency

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Storage & Analytics

Scalable time-series storage with advanced analytics

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ML/AI Capabilities

Real-time classification and custom model deployment

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Clinical Features

HIPAA-compliant data handling for medical applications

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Developer Tools

Comprehensive APIs, SDKs, and integration options

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Device Integration

Supported Devices

Consumer

Consumer-Grade Devices

Emotiv EPOC X (14 channels)
Emotiv Insight (5 channels)
NeuroSky MindWave
Muse 2 (4 channels)
OpenBCI Cyton (8/16 channels)
OpenBCI Ganglion (4 channels)

Consumer devices are perfect for prototyping and educational purposes

Custom

Custom Integration

Lab Streaming Layer (LSL) compatible devices
Custom hardware via SDK
Arduino/Raspberry Pi based systems
Research prototypes
Proprietary systems via adapter


# Example custom device integration
from neurascale.devices import CustomDevice
 
class MyCustomBCI(CustomDevice):
    def __init__(self):
        super().__init__(
            channels=32,
            sampling_rate=1000,
            device_type="EEG"
        )

Key Integration Features

Auto-Discovery: Automatic device detection via USB, Bluetooth, or network
Plug-and-Play: Zero-configuration setup for supported devices
Hot-Swapping: Connect/disconnect devices without restarting
Multi-Device: Simultaneous connection to multiple devices
Impedance Check: Real-time electrode impedance monitoring
Calibration: Automated and manual calibration workflows

Data Processing

Real-Time Signal Processing Pipeline

Processing Capabilities

Filtering

Digital Filtering Options

Bandpass Filters
- Butterworth (2nd-8th order)
- Chebyshev Type I & II
- Elliptic filters
- Custom frequency bands
Notch Filters
- 50/60 Hz powerline noise
- Harmonic removal
- Adaptive notch filtering
Specialized Filters
- Wavelet denoising
- Kalman filtering
- Savitzky-Golay smoothing


# Example filter configuration
filter_config = {
    "type": "butterworth",
    "order": 4,
    "lowcut": 0.5,
    "highcut": 100,
    "sampling_rate": 1000
}

Processing Performance

Metric	Performance	Notes
Latency	Less than 100ms	End-to-end processing
Throughput	10,000+ channels	With GPU acceleration
Sampling Rates	Up to 40 kHz	Device dependent
Filter Delay	Less than 10ms	For real-time filters
Feature Extraction	Less than 50ms	1-second windows

Storage & Analytics

Data Storage Architecture

Storage Features

Time-Series Storage

High-Performance Time-Series Storage

Google Bigtable Integration
- Sub-millisecond latency reads
- Automatic sharding and replication
- Petabyte-scale capacity
- Row-key optimization for time-series
Data Organization
- Channel-based partitioning
- Time-based indexing
- Compression ratios up to 10:1
- Automatic data tiering
Performance Specs
- Write throughput: 1M+ samples/second
- Read latency: Less than 1ms (hot data)
- Storage efficiency: 2 bytes/sample
- Retention: Configurable (1 day to 10 years)


# Example data access
from neurascale.storage import TimeSeriesDB
 
ts_db = TimeSeriesDB()
data = ts_db.query(
    session_id="sess_123",
    channels=[1, 2, 3],
    start_time="2024-01-01T00:00:00",
    end_time="2024-01-01T01:00:00"
)

Data Export

Data Export Options

Standard Formats
- EDF/EDF+ (European Data Format)
- BDF (BioSemi Data Format)
- GDF (General Data Format)
- HDF5 with metadata
- CSV/TSV for analysis
- MAT files for MATLAB
Custom Exports
- Filtered/processed data
- Downsampled versions
- Event-triggered epochs
- Feature matrices
Batch Export
- Scheduled exports
- Multi-session bundles
- Compressed archives
- Cloud storage integration


# CLI export example
neurascale export \
  --session-id sess_123 \
  --format edf \
  --channels 1-32 \
  --output ./exports/

Storage Performance

Storage Tier	Latency	Cost	Use Case
Hot (Bigtable)	Less than 1ms	$$$	Real-time streaming
Warm (Cloud SQL)	Less than 10ms	$$	Recent sessions
Cold (Cloud Storage)	Less than 100ms	$	Long-term archive
Analytics (BigQuery)	1-5s	$$	Complex queries

ML/AI Capabilities

Machine Learning Pipeline

AI/ML Features

Pre-trained Models

Ready-to-Use Models

Motor Imagery Classification
- Left/Right hand movement
- Foot movement detection
- Tongue movement
- 4-class motor imagery
- Accuracy: Greater than 85%
Cognitive State Detection
- Attention/focus level
- Cognitive workload
- Drowsiness detection
- Stress level monitoring
- Accuracy: Greater than 90%
Clinical Applications
- Seizure prediction (10-30min warning)
- Sleep stage classification
- Anesthesia depth monitoring
- Pain level assessment
BCI Paradigms
- P300 speller
- SSVEP detection
- Error-related potentials
- Slow cortical potentials

All models are validated on diverse datasets and include confidence scores

Custom Training

Custom Model Training

Training Infrastructure
- GPU-accelerated training (V100/A100)
- Distributed training support
- Hyperparameter optimization
- Cross-validation pipelines
Supported Frameworks
- TensorFlow/Keras
- PyTorch
- Scikit-learn
- XGBoost/LightGBM
- Custom Python models
Training Features
- Data augmentation
- Transfer learning
- Online learning
- Federated learning support


# Custom model example
from neurascale.ml import ModelTrainer
 
trainer = ModelTrainer()
model = trainer.train(
    data=training_data,
    labels=labels,
    model_type="cnn",
    epochs=100,
    validation_split=0.2
)
 
# Deploy trained model
model.deploy(name="custom_classifier_v1")

ML Performance Metrics

Model Type	Latency	Accuracy	Use Case
Linear Models	Less than 10ms	70-80%	Simple classification
Deep Learning	Less than 50ms	85-95%	Complex patterns
Ensemble	Less than 100ms	90-98%	High accuracy needs
Edge Models	Less than 20ms	80-90%	Local processing

Clinical Features

HIPAA-Compliant Infrastructure

Clinical Compliance Features

Data Security

Healthcare Data Security

Encryption
- AES-256 at rest (Cloud KMS)
- TLS 1.3 in transit
- End-to-end encryption option
- Key rotation every 90 days
Access Control
- Multi-factor authentication
- Role-based permissions
- IP whitelisting
- Session management
Data Isolation
- Patient data segregation
- Multi-tenancy support
- Geographic restrictions
- Data residency compliance
Security Monitoring
- Real-time threat detection
- Anomaly detection
- Failed access alerts
- Security audit reports

Patient Management

Patient Data Management

Patient Records
- Unique patient identifiers
- Demographic information
- Medical history integration
- Consent management
Data Privacy
- De-identification tools
- Pseudonymization support
- Right to deletion (GDPR)
- Data portability
Integration
- HL7 FHIR support
- EHR/EMR connectivity
- DICOM compatibility
- Clinical trial systems


# Patient data example
from neurascale.clinical import PatientManager
 
pm = PatientManager()
patient = pm.create_patient(
    external_id="MRN123456",
    consent_level="research",
    data_retention_days=365
)

Clinical Performance

Feature	Specification	Compliance
Encryption	AES-256	HIPAA §164.312(a)(2)(iv)
Access Logs	7-year retention	HIPAA §164.312(b)
Audit Trail	Immutable ledger	21 CFR Part 11
Backup	Daily, 30-day retention	HIPAA §164.308(a)(7)

Developer Tools

API Architecture

Developer Features

APIs

Comprehensive APIs

REST API v2
- OpenAPI 3.0 specification
- Pagination and filtering
- Rate limiting (1000 req/min)
- Webhook support
GraphQL API
- Type-safe queries
- Real-time subscriptions
- DataLoader optimization
- Schema introspection
WebSocket API
- Real-time data streaming
- Bi-directional communication
- Automatic reconnection
- Binary protocol support
gRPC API
- High-performance streaming
- Protocol buffers
- Multi-language support
- Load balancing


# API example
curl -X POST https://api.neurascale.io/v2/sessions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"device_id": "dev_123", "patient_id": "pat_456"}'

SDKs

Software Development Kits

Python SDK


from neurascale import NeuraScale
 
ns = NeuraScale(api_key="YOUR_API_KEY")
 
# Start recording
session = ns.sessions.create(
    device_id="dev_123",
    patient_id="pat_456"
)
 
# Stream data
for chunk in session.stream():
    print(f"Received {len(chunk)} samples")

JavaScript/TypeScript SDK


import { NeuraScale } from '@neurascale/sdk';
 
const ns = new NeuraScale({ apiKey: 'YOUR_API_KEY' });
 
const session = await ns.sessions.create({
  deviceId: 'dev_123',
  patientId: 'pat_456'
});

Other SDKs
- Java/Kotlin
- C#/.NET
- Go
- Rust
- MATLAB toolbox
- R package

Tools

Development Tools

NeuraScale CLI
- Device management
- Data export/import
- Session monitoring
- Performance testing
Development Console
- API key management
- Usage analytics
- Webhook configuration
- Log viewing
Testing Tools
- Synthetic data generator
- Device simulator
- Load testing suite
- Integration tests
Debugging
- Real-time log streaming
- Event replay
- Performance profiler
- Error tracking


# CLI examples
neurascale device list
neurascale session start --device dev_123
neurascale export --format edf --output ./data/
neurascale simulate --type eeg --channels 32

Developer Resources

Resource	Description	Link
API Documentation	Complete API reference	docs.neurascale.io/api
SDK Examples	Code samples and tutorials	github.com/neurascale/examples
Developer Forum	Community support	forum.neurascale.io
Status Page	Service availability	status.neurascale.io

Platform Features

Feature Categories

Device Integration

Data Processing

Storage & Analytics

ML/AI Capabilities

Clinical Features

Developer Tools

Device Integration

Supported Devices

Consumer

Research

Clinical

Custom

Key Integration Features

Data Processing

Real-Time Signal Processing Pipeline

Processing Capabilities

Filtering

Feature Extraction

Artifact Removal

Real-Time Analysis

Processing Performance

Storage & Analytics

Data Storage Architecture

Storage Features

Time-Series Storage

Session Management

Data Export

Analytics

Storage Performance

ML/AI Capabilities

Machine Learning Pipeline

AI/ML Features

Pre-trained Models

Custom Training

Real-time Inference

AutoML

ML Performance Metrics

Clinical Features

HIPAA-Compliant Infrastructure

Clinical Compliance Features

Data Security

Patient Management

Clinical Workflows

Regulatory

Clinical Performance

Developer Tools

API Architecture

Developer Features

APIs

SDKs

Tools

Integration

Developer Resources

Performance Summary

Platform Capabilities

Speed

Scale

Security

Intelligence