SCiBuT

SCiBuT measures veridical video framerates and synchronizes visual stimulus frame onsets with neural recordings such as EEG and with behavioral responses to provide high temporal precision for experiments involving dynamic visual stimuli.

Key Features:

• Implementation: An open-source MATLAB toolbox for analysis and benchmarking of visual display timing.
• Cigarette Burns markers: Uses black or white squares placed at screen corners as temporal markers for image onset and frame changes.
• Hardware capture: Captures corner light changes with a photodiode connected to an Arduino microcontroller and an analogue input box.
• Framerate measurement: Measures veridical video framerates to detect deviations from intended presentation rates.
• Synchronization: Aligns detected frame onsets with neural recordings (e.g., EEG) and behavioral timestamps.
• Framerate inconsistency detection: Identifies suboptimal hardware setups and subtle framerate discrepancies that can affect EEG oscillations and fine-timing measurements.

Scientific Applications:

• Neuroscience experiments: Validates timing of visual stimuli in studies of neural oscillations and time-sensitive neural dynamics.
• EEG synchronization: Provides precise alignment between visual frame onsets and EEG recordings for event-related and oscillatory analyses.
• Behavioral-neural alignment: Synchronizes behavioral response timestamps with actual visual presentation times.
• Display hardware benchmarking: Benchmarks and diagnoses visual display devices for accurate framerate presentation in experimental setups.

Methodology:

Place black/white corner markers ("Cigarette Burns") on screen; record corner light changes with a photodiode → Arduino → analogue input box; compute frame onset times and veridical framerates in MATLAB and align those timestamps with EEG and behavioral recordings.