Multiview Contrastive Learning for Unsupervised Domain Adaptation in Brain–Computer Interfaces
Bibliographic record
Abstract
Domain adaptation has gained significant attention to address the nonstationarity problem in electroencephalogram (EEG) data in motor imagery (MI) classification. In MI classification, domain adaptation addresses cross-session variations, enhancing the classifier’s generalization capabilities. However, existing methods have struggled to effectively capture both temporal and spatial features, resulting in limited classification accuracy. To tackle this issue, we propose the multi-view adversarial contrastive network (MACNet). The proposed MACNet simultaneously learns spatial and temporal features in two different views: Euclidean and Riemannian. Furthermore, we introduce a multi-level domain mixup technique to enhance domain aligment at both signal and embedding levels. The proposed MACNet method is evaluated on three public datasets. It achieves an accuracy of 83.79% on the BCI Competition IV dataset, 80.00% on the OpenBMI dataset, and 85.83% on SMR datset that outperforms previous methods in cross-session transfer learning.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".