GEERT: General EEG experimentation in real-time, an open source software for BCI real-time experimental designs

A novel open source software application which covers the main steps of a BCI paradigm is proposed: real-time acquisition, online EOG arti- fact removal and band-pass filtering. Being conscious that brain pattern recognition is under constant development, this software offers the option to import external python libraries with a predefined structure in order to include self-developed machine learning approaches, and as experimental designs require event synchronization, a tcp/ip interface is provided. The philosophy behind this application is based on a supervised machine learning approach, and thus offers two modes of interaction: the first allows real-time acquisition and processing in order to generate a database and build models, while the second, provides online signal processing using trained models in order to classify brain patterns. The proposed application is a versatile and easily adaptable to different experimental scenarios while maintain- ing high performance signal processing in real-time. Wearable devices are tools of special interest due to the posibilities they offer for BCI related research such as motor imagery, emotion estimation or attention related studies, which could benefit from open source applications.

1. Real-time acquisition and visualisation of EEG signals.
2. Trigger synchronisation by a tcp/ip interface which allows start/stop recordings remotely.
3. Data recording on EDF file format for electrophysiological signals.
4. Online behaviour labelling interface which labels are synchronised and stored on EDF files.

APP:

COMPATIBLE:

Only for Linux

DEPENDENCIES:

PythonQwt
pyserial
neurodsp
PyQt5
pyqtwebengine
scikit-learn
pandas
ica
scipy
pyqtgraph
pyEDFlib
PyWavelets
lspopt

USE EXAMPLE:

1. add permissions:

sudo chmod 666 /dev/ttyUSB0 (your serial port)

2. Run in one terminal:

python BCI_STANDARD_EXPERIMENT_03.py

3. Set the user filename

4. Set IP and PORT in the app and click the trigger button

5. Run in another terminal:

python

6. Create a client

from COM.trigger_client import trigger_client

tc = trigger_client('IP','PORT')
tc.create_socket()
tc.connect()

Then you are ready to start the recording.

tc.send_msg(b'start')

Labels can be sent asynchronously during the recording and will be stored as events in the EDF user file.

tc.send_msg(b'happy')

To stop the recording and save the temporal series in the user EDF file.

tc.send_msg(b'stop')

CITATION:

@DOI: 10.5281/zenodo.3759306

AUTHOR DETAILS AND CONTACT

Author: Mikel Val Calvo Institution: Dpto. de Inteligencia Artificial, Universidad Nacional de Educación a Distancia (UNED) Email: mikel1982mail@gmail.com