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 at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/grilled-chicken-burger/bci</uri> for noncommercial use.
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 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".