Application of a variation of empirical mode decomposition and teager energy operator to EEG signals for mental task classification
Bibliographic record
Abstract
This paper presents a simple and effective methodology for mental task classification using a novel variation of the empirical mode decomposition (EMD) algorithm and the Teager energy operator applied to electroencephalography (EEG) signals. EEG signals corresponding to various types of mental tasks performed by human subjects are decomposed using the variation of EMD, called Empirical Mode Decomposition-Modified Peak Selection (EMD-MPS), which allows direct separation of the signals into a de-trended component, and a trend, according to a frequency separation criterion. Teager energy operator is then applied to calculate the average energy values of both components obtained after signal decomposition using EMD-MPS. These energy values are used to construct feature vectors, and one-versus-one classification of mental tasks is performed using a simple classifier, namely the 1-NN classifier. An average correct classification rate of 87% is obtained, improving on previous results and thereby also demonstrating the effectiveness of the methodology.
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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".