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Record W4230964735 · doi:10.31224/osf.io/um762

Quantification of Mental Stress using fNIRS Signals

2019· preprint· en· W4230964735 on OpenAlexaboutno aff
Fares Al-Shargie

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsFunctional near-infrared spectroscopyMental arithmeticPrefrontal cortexPsychologyMental stressStress (linguistics)Task (project management)Cognitive psychologyFunctional magnetic resonance imagingAudiologyCognitionNeuroscienceMedicineBlood pressure

Abstract

fetched live from OpenAlex

In this study, we propose functional near infrared spectroscopy (fNIRS) to objectively grade different levels of mental stress. The levels of stress were set based on arithmetic task difficulty, time pressure and negative feedback about peer performance. We examined the proposed approach on twelve human subjects using the Montreal Imaging Stress Task. The experiment results revealed a reduction in cortical activations at prefrontal cortex when stressed, and the differences in hemodynamic response between control condition and under stress were significant with mean p-values of 0.0023, 0.00015 and 0.0004 for arithmetic difficulty level one, two and three, respectively. We thus confirm the feasibility of fNIRS in grading mental stress.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.394
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2019
Admission routes1
Has abstractyes

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