Platform-independent lightweight Python library for designing and conducting timing-critical behavioural and neuroimaging experiments

A Python-based experiment built using PsychoPy for studying decision-making, confidence assessment, and response accuracy under varying conditions of visual dot motion coherence.

StimEval is a cross-platform application for the presentation and evaluation of stimuli. It allows researchers to display images, video, and audio, and collect subjective ratings of responses in a controlled environment.

QDSpy - a Python software for scripting and presenting stimuli for visual neuroscience.

PsychoPy script for a Delayed Matching to Sample (DMS) task. Shows a sample image for 5s, waits 20s, then presents two images for the participant to choose the match. Supports folder-based image loading, numeric order pairing, CSV logging, and flexible timing.

Stimulus presentation platefrom developed at the LARESI Lab

code for 2p imaging experiments that syncs I/O and hardware triggers to neural data acquisition (pupil tracking camera, stimulus presentation, etc.)

These are short example PsychoPy Scripts

These are short example EPrime Scripts

These are short example Presentation (https://www.neurobs.com/) Scripts

Dreye: Receptor Space Manipulation

Contrast Optimized Stimulation player

A stimulus program for visual neuroscience experiments built on psychopy

Reiz is a collection of python modules to present visual and auditory stimuli and send markers with LSL

(Please see debug branch, it will be merged to master soon) In neuroscience research, observing and recording peripheral nerve action potentials has important applications. RETICAP stands for "Real-time Compound Action Potential" (recorder), which has been/is being developed for externally triggered and online-averaged recording of Compound Acti…

Organize stimulus lists for psychological experiments

Stop-signal task in Python

Code for psychology experiments

Psychophysical, eye tracking, fMRI experiments

Stimulus presentation code for the stop-signal anticipation task (Zandbelt & Vink, 2010)