Odor and Subject Identification Using Electroencephalography Reaction to Olfactory
Bibliographic record
Abstract
It is certain that the human brain responds to all kinds of inputs such as feeling, sound, light, and odor. However, to the best of our knowledge, limited works have investigated the response of the human brain to different inputs, especially in eyes-open and eyes-closed (EO & EC) conditions. Due to its fine temporal resolution, portability, noninvasiveness, and low set-up costs, electroencephalography (EEG) is one of the most practical way to evaluate the response of the brain to different inputs. In this study, the brain reactions to olfactory were analyzed, and two identifications were done, which were odor and subject. The brain reactions were captured by EEG from five healthy subjects during smelling of valerian, lotus flower, cheese, and rosewater odors in EO & EC conditions. We tested band power, statistical data, Hjorth parameters, and autoregressive model features and achieved the highest average classification accuracy rates of 96.94% and 99.34% for odor and subject identifications, respectively. The obtained results proved that the olfactory response of the human brain in EO & EC conditions can be reliably used for odor and subject identifications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".