Introduction
Welcome to the official documentation for NeuraScale, a comprehensive Brain-Computer Interface (BCI) platform providing real-time neural data acquisition, processing, and analysis with sub-100ms latency.
Platform Overview
What is NeuraScale?
NeuraScale is a production-ready BCI platform deployed on Google Cloud Platform that enables:
- Universal Device Support: 30+ BCI devices from consumer to research grade
- Real-Time Processing: Sub-100ms latency via Bigtable and Pub/Sub
- Massive Scalability: Handle 10,000+ channels with autoscaling infrastructure
- Clinical Compliance: HIPAA/SOC 2 Type II/GDPR compliant with Secret Manager and KMS encryption
- ML Integration: Real-time inference with GPU-enabled GKE clusters
- Multi-Environment: Separate development, staging, and production projects
- Infrastructure as Code: Full Terraform automation with modular architecture
Core Architecture
Documentation Sections
Getting Started
- Quick Start Guide - Set up NeuraScale in minutes
- System Architecture - Understand the platform design
- Platform Features - Explore comprehensive capabilities
Development & Integration
- API Documentation - REST, GraphQL, WebSocket, and gRPC APIs
- System Modeling - Diagrams and technical specifications
- Device Integration - Connect 30+ BCI devices
- ML/AI Capabilities - Real-time neural classification
Technical Specifications
Supported Devices
Consumer BCIs
- OpenBCI (Cyton, Ganglion, Cyton+Daisy)
- Emotiv (EPOC+, Insight)
- Muse (Muse 2, Muse S)
- NeuroSky MindWave
Research Systems
- g.tec (g.USBamp, g.Nautilus)
- BrainProducts (actiCHamp, LiveAmp)
- ANT Neuro (eego™)
- BioSemi ActiveTwo
Clinical Arrays
- Blackrock (Utah Array, CerePlex)
- Plexon OmniPlex
- Custom LSL streams
Performance Metrics
| Metric | Specification |
|---|---|
| Latency | 50-80ms (typical), <100ms (guaranteed) |
| Sampling Rates | Up to 30 kHz (spikes), 1-2 kHz (LFP), 250-500 Hz (EEG) |
| Channel Count | 8 to 10,000+ channels |
| Data Throughput | 40 MB/s sustained |
| Storage Compression | 10:1 with lossless algorithms |
Operations & Deployment
- Deployment Guide - Local, staging, and production environments
- Security Best Practices - HIPAA/SOC 2/GDPR compliance and data protection
- Clinical Workflows - Healthcare-compliant operations
- Storage & Analytics - Time-series data management
Infrastructure
- Architecture Overview - System design and components
- System Modeling - Visual diagrams and technical flows
- Data Processing - Real-time signal processing pipeline
- Performance Metrics - Platform capabilities and benchmarks
Advanced Features
- Real-time Data Streaming - Sub-100ms latency processing
- Machine Learning Integration - Pre-trained models and custom training
- Developer Tools - APIs, SDKs, and integration options
- Clinical Compliance - HIPAA/SOC 2-compliant infrastructure
Use Cases
Research Applications
- Motor Imagery: Decode movement intentions
- P300 Spellers: Brain-controlled typing
- SSVEP: Steady-state visual stimuli
- Neurofeedback: Real-time brain training
Clinical Applications
- Seizure Detection: Real-time epilepsy monitoring
- Sleep Staging: Automatic sleep analysis
- Stroke Rehabilitation: Motor recovery training
- Locked-in Syndrome: Communication interfaces
Consumer Applications
- Meditation Apps: Track mental states
- Gaming: Mind-controlled games
- Productivity: Focus and attention monitoring
- Wellness: Stress and relaxation tracking
Platform Status
Roadmap
Completed
Foundation & Integration (Phases 1-8)
- Phase 1: Core Infrastructure Setup
- Phase 2: Data Models & Storage Layer
- Phase 3: Basic Signal Processing Pipeline
- Phase 4: User Management & Authentication
- Phase 5: Device Interfaces & LSL Integration
- Phase 6: Clinical Workflow Management
- Phase 7: Advanced Signal Processing
- Phase 8: Real-time Classification & Prediction
Intelligence (Phases 9-12)
- Phase 9: Performance Monitoring & Analytics
- Phase 10: Security & Compliance Layer
- Phase 11: NVIDIA Omniverse Integration
- Phase 12: API Implementation & Enhancement
Infrastructure (Phases 13-16)
- Phase 13: MCP Server Implementation
- Phase 14: Terraform Infrastructure on GCP
- Bigtable for time-series neural data (with autoscaling)
- Pub/Sub topics for signal types (EEG, EMG, ECG, etc.)
- Cloud Run for MCP Server deployment
- Artifact Registry for container images
- Secret Manager for secure configuration
- Phase 15: Kubernetes Deployment on GKE
- Helm charts for Neural Engine services
- Autoscaling with HPA and node pools
- GPU support for ML workloads
- Ingress with TLS termination
- Phase 16: CI/CD Pipeline Enhancement
- GitHub Actions with Workload Identity
- Multi-environment deployment (dev/staging/prod)
- Automated testing and validation
- Infrastructure as Code with Terraform
Community & Support
Getting Help
- Documentation - Comprehensive guides
- GitHub Discussions - Ask questions
- Issue Tracker - Report bugs
- Email Support - Direct assistance
Contributing
We welcome contributions! See our Contributing Guide for:
- Code style guidelines
- Development workflow
- Testing requirements
- Pull request process
Roadmap
Track our progress and upcoming features:
License
NeuraScale is open source under the MIT License. See LICENSE for details.
Built by the NeuraScale Team
Bridging Mind and World Through Advanced Neural Cloud Technology
Last updated: February 2, 2025
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