Dual Contrastive Training and Transferability-Aware Adaptation for Multisource Privacy-Preserving Motor Imagery Classification
Bibliographic record
Abstract
Motor imagery (MI) is one of the brain–computer interface (BCI) paradigms that allows a participant to mentally imagine the movement execution without moving physically by electroencephalogram (EEG). The MI signals vary dramatically among multiple subjects, which makes the MI classification task extremely challenging, because the model trained on one subject may totally fail on another one. Furthermore, privacy concerns always arise as the EEG contains sensitive health and mental information. In this article, we propose an unsupervised multisource-free domain adaptation (DA) algorithm to reduce the discrepancy between the individual MI signals and protect individual privacy simultaneously. Specifically, in the source training phase, we fully leverage the labels of the source data in an instance-contrast fashion guided by a contrastive cross-entropy loss and learn the intrinsic structure inside the data in a category-consistent way by a categorical contrastive loss. In the target adaptation phase, our model only accesses the parameters of the source models instead of the source data for privacy preservation. To achieve this, we propose to ensemble the source models by linear combination and then theoretically explain why the target model performs better than or equal to the arbitrary source model. We further keep our model’s attention to the transferability of the source models by estimating the maximum value of label evidence to prevent noise accumulation when generating pseudo-labels. Sufficient experiments on three datasets with similar properties demonstrate our model outperforms state-of-the-art methods for cross-subject MI classification tasks. Our source code is available athttps://github.com/grilled-chicken-burger/bcifor noncommercial use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".