SANS TRACAS: Design and Evaluation of a Cross-Platform Tool for Conducting Online EEG Experiments
Bibliographic record
Abstract
For nearly a century, electroencephalography (EEG) research has been a crucial tool in the study of brain activity, and this valuable research technique is becoming increasingly accessible due to recent advancements in technology. In this article, we report on a collaborative effort between cognitive neuroscientists and human–computer interaction (HCI) researchers to design, develop, and evaluate Sans Tracas – a cross-platform web application for running EEG experiments online. Using a multidisciplinary and user-centric iterative design approach, Sans Tracas was designed to be easy to use by researchers and study participants alike. To evaluate the feasibility of Sans Tracas for conducting online EEG experiments for people with varying EEG and Brain Computer Interface (BCI) knowledge, we conducted a study with 55 participants, followed by a semi-structured interview with 11 participants to uncover more qualitative insights into the usability and people’s perception of the platform. Results showed that the average number of trials to connect the Muse with Sans Tracas was 2.55 (SD = 3.42). The average time to complete the entire study was 17 minutes (SD = 4 minutes). Participants also found Sans Tracas fun to participate in EEG experiments independently and reported more interest in EEG and BCI research than before the study. We conclude that Sans Tracas is a usable platform for conducting online EEG studies and offers an alternative to traditional in-lab settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".