FATIGUE DETECTION USING THE STRENGTH OF DOMINANT EEG SOURCE: A BEAMFORMING APPROACH
Bibliographic record
Abstract
It is evident that the electroencephalogram (EEG) rhythms are slightly changed when the efficacy of mental activity declines (brain fatigue). Nonetheless, this slight change is not easily detectable by the so far suggested scalp EEG features. The goal of this paper is to propose an EEG-based biomarker, which has a congruity to the mental fatigue variation to detect the transition from non-fatigue to the fatigue mental state. The strength of the dominant EEG source, extracted by minimum variance beamformer (MVB), is proposed here as a discriminative feature to remarkably classify the two mental states. To assess the proposed scheme, EEG signals of 17 volunteers were recorded via 32 electrodes before and after taking an exhausting mental exam (3[Formula: see text]h) and the extracted EEG features were labeled as non-fatigue and fatigue, respectively. After removing the eye-blink effect, the proposed feature along with the conventional EEG features were extracted from the recorded EEGs and then applied to support vector machine (SVM) and 1-nearest neighbor (1NN) classifiers in order to differentiate these two mental states. The best result is achieved by applying the proposed feature to the SVM classifier providing 97.06% classification accuracy which is significantly ([Formula: see text]) superior to its counter parts.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".