Architecture

NeuraScale is a modern BCI platform with a decoupled architecture that separates the user-facing console from the neural data processing engine. The platform combines best-in-class cloud services for optimal performance, security, and developer experience.

Core Architecture Components

1. Console (Frontend) - Next.js application deployed on Vercel for the user interface
2. Authentication - Firebase Auth for secure user authentication and session management
3. User Database - NeonDB (PostgreSQL) for user data, settings, and session metadata
4. Neural Engine - Python-based backend for real-time BCI data processing
5. API Gateway - RESTful and WebSocket APIs connecting frontend to backend services

Implementation Status

System Architecture

Frontend Architecture (Console)

Vercel Deployment

The NeuraScale Console is a modern web application built with Next.js and deployed on Vercel’s edge network for optimal performance and global availability.

Key Benefits:

• Zero-config deployments with Git integration
• Automatic HTTPS and custom domains
• Edge caching and global CDN
• Serverless functions for API routes
• Built-in analytics and monitoring

Firebase Authentication

Firebase provides secure, scalable authentication with support for multiple identity providers and seamless integration with the frontend.

NeonDB (User Data Storage)

NeonDB provides a serverless PostgreSQL database optimized for modern applications with automatic scaling and branching capabilities.

Backend Architecture (Neural Engine)

Device Service

The Device Service manages all BCI device connections and real-time data acquisition.

Technical Specifications:

• Written in Python 3.12 with asyncio
• Uses lock-free ring buffers for data
• Implements backpressure mechanisms
• Sub-100ms latency guarantee

Processing Service

The Processing Service handles all signal processing and feature extraction operations using GCP AI/ML services.

Data Service

The Data Service manages data persistence, retrieval, and analytics using GCP’s multi-tier storage architecture.

Data Flow & Latency

Real-Time Data Pipeline

Latency Budget

Scalability

Horizontal Scaling Architecture

Resource Allocation

Security Architecture

Defense in Depth

Compliance Features

Technology Stack

Deployment Architecture

Multi-Environment Setup

Vercel Deployment Pipeline

Frontend-Backend Integration

API Architecture

The console communicates with the Neural Engine through a well-defined API layer that handles authentication, data streaming, and session management.

Authentication Flow

Disaster Recovery

Backup Strategy:

• NeonDB: Continuous backups with 7-day retention
• Firebase: Automatic daily backups
• Cloud Storage: Multi-region replication for EEG files
• Configuration: Git-based version control

Performance Optimization

Optimization Techniques

# Shared memory segments
buffer = mmap.mmap(-1, size)
# Direct memory access
numpy_array = np.frombuffer(buffer)

# NumPy with MKL backend
# AVX2/AVX-512 instructions
filtered = np.convolve(data, kernel, mode='same')

# CuPy for GPU processing
import cupy as cp
gpu_data = cp.asarray(cpu_data)
gpu_fft = cp.fft.fft(gpu_data)

# AsyncIO for concurrent operations
async def process_streams(devices):
    tasks = [process_device(d) for d in devices]
    await asyncio.gather(*tasks)

Future Enhancements

Roadmap

Research Areas

• Neuromorphic Computing - Brain-inspired hardware integration
• Spiking Neural Networks - Event-based processing
• Reservoir Computing - Efficient temporal processing
• Brain-Computer Interface Standards - Industry standardization

Related Documentation

• API Documentation - Complete API reference
• Neural Management System - Neural Engine details
• Security - Security and compliance details
• Contributing Guide - Development guidelines