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PiEEG_XR.

Transfer your Emotions and mimic to Virtual Reality
It's a page about Hardware.
Software - PiEEG Server also details in docs

IronBCI Demo
IronBCI Demo

🛠️ Mask Details. Electrodes Location.

10 flat dry silver-silver chloride (Ag/AgCl) electrodes are specialized biosignal sensors used for recording surface biopotentials, ECG/EMG monitoring, and EEG research without requiring messy conductive gels

IronBCI Demo

Mask - Material:silicone

IronBCI Demo

🛠️ Technical Specifications

Hardware Architecture

  • Analog-to-Digital Converter (ADC): Dual ADS1299 (supporting up to 16 (depends of version) simultaneous channels of high-resolution biopotential data).
  • Microcontroller (MCU): STM32WB series (Dual-core ARM Cortex-M4/M0+ for robust application processing and dedicated wireless stacks).
  • Wireless Connectivity: Bluetooth Low Energy (BLE 5) for low-latency, ultra-low-power data transmission.

Signal Integrity & Electrodes

  • Ultra-Low Noise: 1.0 µVₚₚ (Peak-to-Peak Noise), ensuring clean signal baselines even in challenging environments.
  • Electrode Compatibility: Supports both Gel (wet) and Dry electrode systems for flexible deployment.

Software Ecosystem

  • Backend Server: PiEEG Server (for seamless data streaming, processing, and visualization).

Warnings

Warning

PiEEG_XR is not medical device. You are fully responsible for your personal decision to purchase this device and, ultimately, for its safe use. PiEEG_XR is not a medical device and has not been certified by any government regulatory agency for use with the human body. Use it at your own risk.

Caution

The device must operate only from a battery - 5 V. Complete isolation from the mains power is required! The device MUST not be connected to any kind of mains power, via USB or otherwise.
Power supply - only battery 5V, please read the liability

Contacts

https://pieeg.com/
pieeg@pieeg.com

Support

PiEEG Discord

To cite El Abbassi, Y.; Rakhmatulin, I. PiEEG XR: A WebXR Brain-Computer Interface Platform for Neural-Adaptive Avatar Control in Mixed Reality. Preprints 2026, 2026060016. https://doi.org/10.20944/preprints202606.0016.v1

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