Multimodal deep learning approach for joint EEG-EMG data compression and\n classification
Bibliographic record
Abstract
In this paper, we present a joint compression and classification approach of\nEEG and EMG signals using a deep learning approach. Specifically, we build our\nsystem based on the deep autoencoder architecture which is designed not only to\nextract discriminant features in the multimodal data representation but also to\nreconstruct the data from the latent representation using encoder-decoder\nlayers. Since autoencoder can be seen as a compression approach, we extend it\nto handle multimodal data at the encoder layer, reconstructed and retrieved at\nthe decoder layer. We show through experimental results, that exploiting both\nmultimodal data intercorellation and intracorellation 1) Significantly reduces\nsignal distortion particularly for high compression levels 2) Achieves better\naccuracy in classifying EEG and EMG signals recorded and labeled according to\nthe sentiments of the volunteer.\n
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".