This document provides a comprehensive overview of the technology stack powering Brain-Forge, including installation instructions, configuration details, and integration guidelines.
- Core Python Stack
- Neural Simulation & Modeling
- Real-time Processing & Streaming
- Visualization & UI
- Hardware Integration
- Database & Storage
- Development & DevOps
- GPU Acceleration
- Installation Guide
# Core scientific libraries
numpy>=1.21.0 # Fundamental array operations and linear algebra
scipy>=1.7.0 # Advanced scientific computing algorithms
pandas>=1.3.0 # Data manipulation and analysis
scikit-learn>=1.0.0 # Machine learning algorithms
# Neuroimaging & brain analysis
mne>=1.0.0 # MEG/EEG data processing and analysis
nilearn>=0.8.0 # Machine learning for neuroimaging
dipy>=1.4.0 # Diffusion imaging and brain connectivity
nibabel>=3.2.0 # Neuroimaging file format support- NumPy: Foundation for all numerical computing, provides N-dimensional arrays
- SciPy: Statistical functions, optimization, signal processing
- MNE: Industry standard for MEG/EEG analysis with comprehensive preprocessing
- Nilearn: Statistical learning on neuroimaging data with scikit-learn integration
# Neural network simulation
brian2>=2.4.0 # Spiking neural network simulator
nest-simulator>=3.0 # Large-scale neural network simulation
neuron>=8.0 # Detailed biophysical neural modeling
# Deep learning frameworks
torch>=1.10.0 # PyTorch deep learning framework
torchvision>=0.11.0 # Computer vision models and datasets
tensorflow>=2.7.0 # TensorFlow machine learning platform
keras>=2.7.0 # High-level neural network API- Brian2: Equation-based modeling for spiking neural networks
- NEST: Scalable simulation of heterogeneous neural networks
- NEURON: Detailed compartmental modeling of neurons
- PyTorch/TensorFlow: Deep learning for pattern recognition and neural decoding
# Brian2 spiking neural network
from brian2 import *
# Create a simple neuron model
eqs = '''
dv/dt = (I-v)/tau : volt
I : volt
tau : second
'''
G = NeuronGroup(100, eqs, threshold='v>-50*mV', reset='v=-70*mV')# Real-time data streaming
pylsl>=1.14.0 # Lab Streaming Layer for real-time data
timeflux>=0.6.0 # Real-time neurophysiological computing
asyncio # Built-in asynchronous programming
# Signal processing
pywavelets>=1.1.0 # Wavelet transforms for compression
spectrum>=0.8.0 # Spectral analysis methods- PyLSL: Multi-modal sensor synchronization with microsecond precision
- Timeflux: Node-based real-time processing pipeline
- AsyncIO: Non-blocking I/O for concurrent data handling
import pylsl
from timeflux.core.node import Node
class BrainDataProcessor(Node):
def update(self):
# Process incoming brain data in real-time
if self.i.ready():
data = self.i.data
processed = self.process_neural_signals(data)
self.o = processed# Scientific plotting
matplotlib>=3.5.0 # Publication-quality static plots
seaborn>=0.11.0 # Statistical data visualization
plotly>=5.0.0 # Interactive web-based plotting
# 3D brain visualization
mayavi>=4.7.0 # 3D scientific data visualization
pyvista>=0.32.0 # 3D plotting and mesh analysis
vtk>=9.0.0 # Visualization toolkit
# Interactive dashboards
streamlit>=1.2.0 # Web app framework for data science
dash>=2.0.0 # Interactive analytical web applications
jupyter>=1.0.0 # Interactive computing notebooks- 3D Brain Rendering: Real-time brain activity visualization
- Interactive Dashboards: Web-based control interfaces
- Scientific Plotting: Publication-ready figures and animations
import pyvista as pv
import numpy as np
# Create 3D brain visualization
plotter = pv.Plotter()
# Load brain mesh and add neural activity
brain_mesh = pv.read('brain_template.vtk')
activity_data = np.random.random(brain_mesh.n_points)
plotter.add_mesh(brain_mesh, scalars=activity_data,
opacity=0.8, cmap='viridis')
plotter.show()# Hardware interfaces
pyserial>=3.5 # Serial communication for devices
pyusb>=1.2.0 # USB device interface
bleak>=0.13.0 # Bluetooth Low Energy support
# Parallel processing
multiprocessing # Built-in parallel processing
threading # Built-in threading support
joblib>=1.1.0 # Efficient parallel computing- OPM Helmets: 306-channel magnetometer arrays via serial/USB
- Kernel Optical: Flow/Flux helmets with optical data streaming
- Accelerometers: Motion tracking via I2C/Bluetooth interfaces
import serial
import pylsl
class OMPHelmetInterface:
def __init__(self, port='/dev/ttyUSB0', channels=306):
self.serial = serial.Serial(port, 115200)
self.outlet = pylsl.StreamOutlet(
pylsl.StreamInfo('OMP_Data', 'MEG', channels, 1000)
)
def stream_data(self):
while True:
raw_data = self.serial.read(self.channels * 4) # 4 bytes per float
neural_data = np.frombuffer(raw_data, dtype=np.float32)
self.outlet.push_sample(neural_data)# Data storage formats
h5py>=3.4.0 # HDF5 high-performance data storage
zarr>=2.10.0 # Chunked, compressed array storage
sqlalchemy>=1.4.0 # SQL database ORM
# Cloud storage integration
boto3>=1.20.0 # AWS SDK for cloud storage
google-cloud-storage>=1.44.0 # Google Cloud Platform storage- HDF5: High-performance binary data storage for neural signals
- Zarr: Cloud-optimized chunked storage for large datasets
- SQL Databases: Metadata and configuration management
import h5py
import zarr
# HDF5 storage for neural data
with h5py.File('brain_data.h5', 'w') as f:
f.create_dataset('neural_signals', data=neural_data,
compression='gzip', compression_opts=9)
f.create_dataset('timestamps', data=timestamps)
# Zarr for cloud-optimized storage
store = zarr.DirectoryStore('brain_data.zarr')
root = zarr.group(store=store)
root.create_dataset('signals', data=neural_data, chunks=(1000, 306))# Testing frameworks
pytest>=6.2.0 # Testing framework
pytest-cov>=3.0.0 # Coverage reporting
pytest-asyncio>=0.18.0 # Async testing support
# Code quality tools
black>=21.0.0 # Code formatting
flake8>=4.0.0 # Code linting
mypy>=0.910 # Static type checking
pre-commit>=2.15.0 # Pre-commit hooks# Documentation generation
sphinx>=4.0.0 # Documentation generator
sphinx-rtd-theme>=1.0.0 # Read the Docs theme
myst-parser>=0.15.0 # Markdown parser for Sphinx# Docker stack components
- Docker & Docker Compose # Container orchestration
- NVIDIA Container Toolkit # GPU support in containers
- Kubernetes # Production scaling# GitHub Actions workflows
name: Brain-Forge CI/CD
on: [push, pull_request]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: 3.9
- name: Install dependencies
run: pip install -r requirements/dev.txt
- name: Run tests
run: pytest tests/ --cov=brain_forge
- name: Code quality checks
run: |
black --check brain_forge/
flake8 brain_forge/
mypy brain_forge/# CUDA acceleration libraries
cupy>=9.0.0 # GPU-accelerated array library
numba>=0.54.0 # JIT compilation with CUDA support
pycuda>=2021.1 # Direct CUDA programming interface# ROCm installation for AMD GPUs
rocm-docs-core # ROCm documentation and tools
hip-dev # HIP development tools
rocblas-dev # GPU-accelerated BLASimport cupy as cp
import numpy as np
# GPU-accelerated neural signal processing
def gpu_filter_signals(signals, sample_rate=1000):
# Move data to GPU
gpu_signals = cp.asarray(signals)
# Apply bandpass filter on GPU
from cupyx.scipy import signal as gpu_signal
b, a = gpu_signal.butter(4, [1, 100], btype='band',
fs=sample_rate)
filtered = gpu_signal.filtfilt(b, a, gpu_signals)
# Return to CPU
return cp.asnumpy(filtered)# Minimum system requirements
- Python 3.8+
- RAM: 16GB (32GB recommended)
- Storage: 100GB available space
- GPU: CUDA-compatible (optional but recommended)# Create virtual environment
python -m venv brain-forge-env
source brain-forge-env/bin/activate # Linux/Mac
# brain-forge-env\Scripts\activate # Windows
# Install core scientific stack
pip install numpy>=1.21.0 scipy>=1.7.0 pandas>=1.3.0
pip install scikit-learn>=1.0.0# Install neuroimaging tools
pip install mne>=1.0.0 nilearn>=0.8.0 dipy>=1.4.0
pip install nibabel>=3.2.0 pywavelets>=1.1.0# Install simulation frameworks
pip install brian2>=2.4.0
pip install nest-simulator>=3.0
pip install neuron>=8.0# PyTorch (with CUDA support)
pip install torch>=1.10.0 torchvision>=0.11.0 --index-url https://download.pytorch.org/whl/cu118
# TensorFlow
pip install tensorflow>=2.7.0# Install visualization libraries
pip install matplotlib>=3.5.0 seaborn>=0.11.0 plotly>=5.0.0
pip install pyvista>=0.32.0 mayavi>=4.7.0
pip install streamlit>=1.2.0 dash>=2.0.0# Install hardware interfaces
pip install pylsl>=1.14.0 pyserial>=3.5 pyusb>=1.2.0
pip install bleak>=0.13.0 timeflux>=0.6.0# For NVIDIA GPUs
pip install cupy-cuda118>=9.0.0 # Adjust CUDA version as needed
pip install numba>=0.54.0
# For AMD GPUs (requires ROCm installation)
pip install cupy-rocm-5-0>=9.0.0FROM python:3.9-slim
# Install system dependencies
RUN apt-get update && apt-get install -y \
build-essential \
git \
&& rm -rf /var/lib/apt/lists/*
# Install Brain-Forge
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . /app
WORKDIR /app
RUN pip install -e .
EXPOSE 8000
CMD ["uvicorn", "brain_forge.api:app", "--host", "0.0.0.0", "--port", "8000"]#!/usr/bin/env python3
"""Verify Brain-Forge installation"""
def verify_installation():
try:
import numpy
print(f"✅ NumPy {numpy.__version__}")
import mne
print(f"✅ MNE {mne.__version__}")
import brian2
print(f"✅ Brian2 {brian2.__version__}")
import pyvista
print(f"✅ PyVista {pyvista.__version__}")
try:
import cupy
print(f"✅ CuPy {cupy.__version__} (GPU acceleration available)")
except ImportError:
print("⚠️ CuPy not installed (GPU acceleration disabled)")
print("\n🎉 Brain-Forge installation verified successfully!")
except ImportError as e:
print(f"❌ Installation error: {e}")
if __name__ == "__main__":
verify_installation()# Efficient memory usage for large neural datasets
import numpy as np
from contextlib import contextmanager
@contextmanager
def memory_mapped_data(filename, shape, dtype=np.float32):
"""Memory-mapped access to large neural datasets"""
mmap = np.memmap(filename, dtype=dtype, mode='r', shape=shape)
try:
yield mmap
finally:
del mmap # Clean up memory mappingfrom joblib import Parallel, delayed
import multiprocessing
def parallel_signal_processing(signals, n_jobs=-1):
"""Parallel processing of neural signals across channels"""
n_jobs = n_jobs if n_jobs > 0 else multiprocessing.cpu_count()
results = Parallel(n_jobs=n_jobs)(
delayed(process_channel)(channel_data)
for channel_data in signals
)
return np.array(results)Issue: ImportError: No module named 'mne'
# Solution: Install with conda for better dependency resolution
conda install -c conda-forge mneIssue: CUDA out of memory errors
# Solution: Process data in smaller chunks
def process_in_chunks(data, chunk_size=1000):
for i in range(0, len(data), chunk_size):
chunk = data[i:i+chunk_size]
yield process_chunk(chunk)Issue: Real-time processing delays
# Solution: Use async processing with proper buffering
import asyncio
async def async_signal_processor():
while True:
data = await get_next_data_chunk()
processed = process_signals(data)
await send_results(processed)
await asyncio.sleep(0.001) # 1ms delayWe welcome contributions to improve Brain-Forge's technology stack:
- Performance Optimizations: GPU acceleration improvements
- New Hardware Support: Additional device drivers
- Visualization Enhancements: Advanced 3D rendering features
- Documentation: Usage examples and tutorials
See our Contributing Guide for detailed instructions.
Last Updated: July 2025
Version: 0.1.0-dev
Maintainers: Brain-Forge Development Team