8-Channel EEG System

This repository contains the complete design and source files for a custom 8-Channel EEG System (Electroencephalography) based on the Raspberry Pi Pico (RP2350A) and the Analog Devices AD7779 24-bit ADC.

The project is modular and covers the entire signal chain: from physical schematics and microcontroller firmware to data acquisition and visualization on the PC.

Project Structure

The repository is organized into three main sections:

• 0_hardware/: KiCad projects for schematics and PCB layouts.
• 1_firmware/: C source code to be flashed onto the hardware.
• 2_pc_datahandler/: Python framework for control, data acquisition (LSL/HDF5), live plotting, and analysis.

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0_Hardware

The hardware folder contains all electronic design files (KiCad).

Key Components:

• MCU: Custom desgin based on the RP2350A.
• ADC: AD7779 (8-Channel, 24-Bit Sigma-Delta ADC).
• Signal Conditioning: Variable gain settings via digital potentiometers (AD5142A).
• Modules: Separate schematics for power management, impedance measurement, sensor interfaces, and active shielding reference.

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1_Firmware

The firmware runs on the RP2350A and handles SPI communication with the ADC, I2C communication with digital potentiometers, and high-speed data transfer to the PC via USB.

Features:

• Sampling Rate: Configurable sampling rate for the AD7779.
• Drivers: Implemented for AD7779 (SPI), AD5142A (I2C), and GPIO shielding control.
• Protocol: USB packet protocol with header/footer framing.
• Synchronization: Microsecond-precision timestamping for every data packet.

Build Instructions: Prerequisites: Pico SDK and CMake.

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2_PC_DataHandler

This folder contains the Python-based host software to interface with the EEG hardware. It manages the USB connection, decodes the raw data stream, and integrates with the Lab Streaming Layer (LSL) ecosystem.

Features:

• Data Acquisition: Reading from the USB serial port.
• LSL Integration: Streams data as an 8-channel EEG signal onto the local network for use with tools like LabRecorder or our live plotter.
• Visualization: Real-time plotting of time-series data.
• Analysis: Signal quality verification.

This project uses uv for fast and reliable dependency management.

Create a virtual environment and sync dependencies: bash uv sync