Promise for Personalized Diagnosis? Assessing the Precision of Wireless Consumer-Grade Electroencephalography across Mental States
Bibliographic record
Abstract
In the last decade there has been significant growth in the interest and application of using EEG (electroencephalography) outside of laboratory as well as in medical and clinical settings, for more ecological and mobile applications. However, for now such applications have mainly included military, educational, cognitive enhancement, and consumer-based games. Given the monetary and ecological advantages, consumer-grade EEG devices such as the Emotiv EPOC have emerged, however consumer-grade devices make certain compromises of data quality in order to become affordable and easy to use. The goal of this study was to investigate the reliability and accuracy of EPOC as compared to a research-grade device, Brainvision. To this end, we collected data from participants using both devices during three distinct cognitive tasks designed to elicit changes in arousal, valence, and cognitive load: namely, Affective Norms for English Words, International Affective Picture System, and the n-Back task. Our design and analytical strategies followed an ideographic person-level approach (electrode-wise analysis of vincentized repeated measures). We aimed to assess how well the Emotiv could differentiate between mental states using an Event-Related Band Power approach and EEG features such as amplitude and power, as compared to Brainvision. The Emotiv device was able to differentiate mental states during these tasks to some degree, however it was generally poorer than Brainvision, with smaller effect sizes. The Emotiv may be used with reasonable reliability and accuracy in ecological settings and in some clinical contexts (for example, for training professionals), however Brainvision or other, equivalent research-grade devices are still recommended for laboratory or medical based applications.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".