Movie Watching Task

Field	Value
Name	Movie Watching Task
Version	main (1.0)
URL / Repository	https://github.com/TaskBeacon/Movie
Short Description	A passive video-watching task with a centered movie clip for EEG studies
Created By	Zhipeng Cao (zhipeng30@foxmail.com)
Date Updated	2025/06/23
PsyFlow Version	0.1.0
PsychoPy Version	2025.1.1
Modality	EEG
Language	Chinese
Voice Name	zh-CN-YunyangNeural

Note

The movie is not publicly available due to potential copyright issues.

1. Task Overview

The Movie Presentation Task is a naturalistic EEG paradigm where participants passively watch a centered movie clip on screen. Unlike full-screen video presentations, the movie is rendered at a fixed visual size while the background remains visible. This format preserves spatial context and minimizes abrupt visual transitions, supporting naturalistic stimulus presentation for continuous EEG data acquisition. No behavioral responses are required; only onset and offset triggers are logged for synchronization.

2. Task Flow

Block-Level Flow

Step	Description
Load Config	Load YAML-based task settings for subject info, stimuli, window, triggers
Collect Subject Info	Prompt for subject ID, name, age, and gender
Setup Triggers	Configure serial trigger output via loop:// or COM
Initialize Window/Input	Create full-screen PsychoPy window and keyboard
Load Stimuli	Load instructions and centered movie video; convert to voice
Show Instructions	Present visual and audio instructions; wait for spacebar
Countdown	Display a 3-second countdown before video starts
Movie Playback	Show centered video stimulus with triggers on start and end
Show Goodbye	Display thank-you screen
Save Data	Save trial metadata in CSV format
Close	Close serial port and PsychoPy session

Trial-Level Flow

Step	Description
Movie Presentation	Show presentch.mp4 (22.1 × 12.4 deg) centered on screen for 204 seconds
Trigger Onset	Send EEG trigger at video start (movie_onset = 1)
Trigger Offset	Send EEG trigger at video end (movie_offset = 2)
Trial Logging	Save trial condition, duration, and triggers

Other Logic

Component	Description
None	No adaptive logic, dynamic trial structure, or stimulus randomization present

This task is entirely passive and fixed across all participants.

3. Configuration Summary

a. Subject Info

Field	Meaning
subject_id	Participant ID (101–999)
subname	Participant name (pinyin)
age	Age (5–60)
gender	Gender (Male/Female)

b. Window Settings

Parameter	Value
size	[1920, 1080]
units	deg
screen	1
bg_color	black
fullscreen	True
monitor_width_cm	60
monitor_distance_cm	72

c. Stimuli

Name	Type	Description
movie	movie	presentch.mp4, centered at 22.1 × 12.4 deg (not full-screen)
instruction_text	textbox	Pre-task guidance with keypress to continue
good_bye	textbox	End-of-task thank-you message
fixation	text	Central fixation (not used during movie playback)

d. Timing

Phase	Duration (s)
movie	204

e. Triggers

Event	Code
Experiment Start	98
Experiment End	99
Block Start	100
Block End	101
Movie Onset	1
Movie Offset	2

4. Methods

Participants engaged in a passive viewing task, during which they watched a 204-second movie clip (The present) presented at the center of the screen within a fixed visual angle (22.1 × 12.4 degrees). While the display window occupied the full screen, the movie itself was embedded within it rather than scaled to full-screen size. This design allows for reduced visual noise and stable peripheral context. Participants were instructed to remain still, focus on the screen, and minimize eye movements and blinking. No responses were required during the video. A trigger was sent at the start (movie_onset) and end (movie_offset) of the movie to synchronize EEG data collection. The experiment consisted of one block and one trial.

5. References

Alexander, L. M., Escalera, J., Ai, L., Andreotti, C., Febre, K., Mangone, A., … & Milham, M. P. (2017). An open resource for transdiagnostic research in pediatric mental health and learning disorders. Scientific data, 4(1), 1-26.

Shirazi, S. Y., Franco, A., Scopel Hoffmann, M., Esper, N. B., Truong, D., Delorme, A., … & Makeig, S. (2024). HBN-EEG: The FAIR implementation of the Healthy Brain Network (HBN) electroencephalography dataset. bioRxiv, 2024-10.