Open biosignal hardware
Eight acquisition platforms—from Raspberry Pi shields to a simultaneous 32-channel system—built to keep the signal visible and the pipeline open.
4 × AD7771 / 24-BITSTM32H7 / CORTEX-M732 CH / SIMULTANEOUS01 / Design position
PiEEG hardware is designed around a continuous, inspectable path: electrodes acquire the body's electrical activity, the board converts it into data, and an open software stack carries it into the system you are building.
02 / Channel atlas
Move between 8, 16, and 32 channels. The interface reveals which PiEEG instruments match that acquisition density.
Channel atlas / choose density
The IronBCI-32 combines four 8-channel AD7771 ADCs with an STM32H7 for simultaneous 32-channel capture.
03 / Instrument rack
Each platform changes where computation happens, how the signal travels, and how freely the experiment can move.
Thirty-two channels. One simultaneous field of view.
Four 8-channel AD7771 ADCs meet an STM32H7 ARM Cortex-M7 for highly accurate, simultaneous biosignal acquisition.
Double the channels. Keep the computer in the loop.
Sixteen-channel acquisition integrated directly with Raspberry Pi 5 for more complex experiments and on-device processing.
Dense enough for the lab. Small enough to leave it.
Two ADS131M08 24-bit ADCs and a Seeed Studio XIAO ESP32-S3 create a portable, battery-powered acquisition system with Bluetooth streaming.
The shortest path from electrodes to Python.
Eight-channel biosignal acquisition and processing directly on Raspberry Pi 3, 4, or 5 with an open-source Python SDK.
A biosignal instrument designed to move.
A compact 50 mm acquisition system with Bluetooth Low Energy 5, Python and Android SDKs, and a 3D-printable headset design.
Embedded acquisition for embedded ideas.
An eight-channel BCI shield for STM32 NUCLEO-WB55 with Python and mobile SDKs for learning, prototyping, and research.
Learn electronics through signals from the body.
A low-power Arduino UNO R4 WiFi shield with programmable gain, impedance measurement, SPI transfer, and a Python SDK.
Capture the signal where machine learning runs.
An eight-channel shield for real-time biosignal reading and processing on Jetson Nano, designed for machine-learning workflows.
04 / Field comparison
| Instrument | Channels | Compute | Transfer / rate | Signal family |
|---|---|---|---|---|
| IronBCI-32 | 32 | STM32H7 | 500 Hz / channel | EEG · EMG · ECG |
| PiEEG-16 | 16 | Raspberry Pi 5 | 250 SPS–16 kSPS | EEG · EMG · ECG |
| Octopus 16 | 16 | ESP32-S3 | Wireless stream | EEG · EMG · ECG |
| PiEEG | 8 | Raspberry Pi 3/4/5 | 250 SPS–16 kSPS | EEG · EMG · ECG |
| IronBCI | 8 | BLE5 wireless | 250 SPS | EEG · EMG · ECG |
| MicroBCI | 8 | STM32 NUCLEO-WB55 | 250 SPS | EEG · EMG · ECG |
| ardEEG | 8 | Arduino UNO R4 WiFi | SPI transfer | EEG · EMG · ECG |
| JNEEG | 8 | Jetson Nano | 250 SPS–16 kSPS | EEG · EMG · ECG |
05 / Complete the circuit
Caps, electrodes, gel, and multi-signal accessories complete the path between the body and the acquisition system.
Dry electrodes · Adjustable
A02
Wet electrodes · Includes gel
A03
Sintered Ag/AgCl electrodes
A04
32 wet electrodes · 2 references
A05
100 ml · wet electrodes
A06
Multi-signal · PiEEG compatible
06 / Begin with the signal