The easiest way to the neuroscience world with the shield for RaspberryPi - PIEEG. Real-Time signal processing GUI in python and for robot control. Open-source.
This project is the result of several years of work on the development of BCI. We believe that the easiest way to get started with biosignals is to use a shield. We will try to reveal the process of reading EEG signals as fully and clearly as possible.
- How it Works
- Noise measure
- Device pinout
- Description of code
- Video-hardware and signal processing demonstration
- For beginners
- Citation
- Contacts
1.1.Read_data.c C script for read data in real-time and save to txt file
1.2.Read_data.cpp C++ script for read data in real-time and save to txt file
real_time.py GUI python script for read data in real-time
robot_control.py script to control a robot by blinking
Connect the shield to Raspberry PI 3 or RaspberryPI4 and after that connect the device to a battery (power supply) and connect electrodes.
Full galvanic isolation from mains required.
This also applies to the monitor. Use only a monitor that is powered by the RaspberryPI, as in the picture below, left. Electrodes positioned according to International 10-20 system, right.
Shiled connceted with raspbberryPI only in the netxt points
43 +5V
44 GND
37 MOSI
34 MISO
35 CLKL
36 CS
Chewing artifact (4-3-2-1) and blinking (only 4 times), in real-time for 8 electrodes via real_time.py
Blinking artifact, after Chewing. Raw data
Raw data with band-pass filter (1-40Hz)
Alpha wave detection eyes open, eyes closed
Python script dont allow reading data from ADS1299 with the frequency of 250 Hz. Necessary to use .c or .cpp scripts for reading data in real-time and python for signal processing and vialuzation.
During the measurement, in addition to artifacts caused by muscle activity, be concerned about the increased resistance between the body and the floor. For example, in the picture below, the moment when the feet touch the floor with and without an insulated shoe. Without insulated shoes - increased noise is noticeable
PIEEG: Turn a Raspberry Pi into a Brain-Computer-Interface to measure biosignals. arXiv:2201.02228