QT-nback_study

Python/PyQt5 n-back task (baseline, 1-4 back) that emits Lab Streaming Layer (LSL) marker strings for each trial.

Contents

• n-back/mainExperiment.py: experiment GUI and logic
• n-back/Correct.mp3, n-back/Incorrect.mp3: tutorial feedback sounds

Requirements

• Python 3.9+ with GUI support
• Packages in requirements.txt
• Audio output for tutorial feedback
• Optional: an LSL receiver (for example, LabRecorder) if you want to record markers

Tested environment

Update this section with the exact environment used for your release.

• OS: Windows (developed on Windows 11 Professional)
• Python: 3.x
• PyQt5: 5.15.x
• pylsl: 1.16.x
• numpy: 1.x
• playsound: 1.2.2

To capture exact versions on the acquisition machine:

python -c "import platform,sys,importlib.metadata as md; print(platform.platform()); print(sys.version); [print(p, md.version(p)) for p in ['PyQt5','pylsl','numpy','playsound']]"

Installation

python -m venv .venv
# Windows PowerShell
. .venv\Scripts\Activate.ps1
# macOS/Linux
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt

Running

python n-back/mainExperiment.py

Controls:

• Space: respond to a match
• Q: quit the experiment window

LSL output

Markers are emitted on the n-backMarkers stream as strings. Examples:

• Started tutorial n-back
• Steps:2 KeyPress:True Matched:True
• Finished n-back

Reproducibility notes

• The stimulus sequence is randomized using random and numpy; set seeds if you need deterministic sequences.
• This code does not save data to disk. Record markers with an LSL consumer if you need logs.

Docker (optional)

The GUI requires a display server. On Linux hosts with X11:

docker build -t qt-nback .
xhost +local:docker
docker run --rm -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix qt-nback