Open design and validation of a reproducible videogame controller for MRI and MEG
Bibliographic record
Abstract
Playing video games in a neuroimaging environment is both scientifically promising and technically challenging. Primary among these challenges is the need to use scanner-compatible devices to register player inputs, which limits the type of games that can be comfortably played in a scanner and often reduces the ecological validity of video game tasks. In this paper, we introduce an MRI- and MEG-compatible video game controller that is made exclusively of 3D-printed and commercially available parts, and we release the design files and documentations in the goal of making its production accessible to any research team with minimal engineering resources. In line with the open science philosophy, we made this work accessible under an Open Source Hardware license that aims to promote accessibility and reproducibility. Additionally, we validated the responsiveness and scanner-compatibility of our controller by comparing it to a reference, non-MRI compatible controller, and by assessing the quality of the data recorded with and without the use of the said controller. The analysis of response latencies showed reliable button press accuracies. A higher latency was detected on button releases, both for long and short button presses although this effect was small enough as not to affect gameplay in most situations. Analysis of subject motion during fMRI recordings of various tasks showed that the use of our controller didn’t increase the amount of motion produced. We hope that this tool will stimulate further neuroimaging studies of video games tasks by improving both their accessibility and their validity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".