Detecting Changes in Cognitive Load Through Audified EEG
Bibliographic record
Abstract
Concussion is an increasing concern, especially with the popularity of contact sports. New research shows the dangers of letting concussions go undetected and untreated. Current assessment methods are lacking in their reliability to detect concussions and track healing. An objective assessment method to evaluate concussion would be a great benefit to society. Electroencephalography (EEG) consists of data measured from electrical signals that can give insight into the activity and health of the brain. The first step in assessing concussion through EEG is to understand the signal properties while performing different cognitive tasks. While these signals are often displayed graphically, they can also be converted to sound (audification) to translate the data into a more intuitive medium. By using EEG to understand how the brain processes information under different levels of cognitive load and interpreting these data through audification, this research can pave the way for audified EEG being used to assess brain health, specifically concussion. Seventy-five untrained participants were asked to identify high cognitive load by listening to audified EEG data relating to different tasks. Eighty-four percent of participants were able to detect the difference between high and low cognitive load, when listening to audified samples of EEG data. Clinical Relevance - This work provides evidence that audified EEG can be used to differentiate cognitive load conditions by untrained observers. The same approach could be used to assess concussion as brain activity differs immediately post-injury.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".