Explainable Orthogonal Attention Networks for EEG-based Analysis: Leveraging Disentangled Representations to Enhance Diagnosis
Bibliographic record
Abstract
The complexity of EEG data presents significant challenges for accurate diagnosis in neurological conditions such as Alzheimer’s disease. In this paper, we introduce Explainable Orthogonal Attention Networks, a novel approach for EEG-based analysis that decouples spatial and temporal features to more effectively capture disease-related neural patterns. By leveraging orthogonal attention mechanisms, our model independently processes spatial relationships across EEG channels and temporal dynamics, enhancing both explainability and predictive performance. Our approach outperforms baselines, achieving superior performance in objective metrics, with a 14% relative improvement, while offering insights into the neural mechanisms underlying Alzheimer’s disease. Using attention maps and spectral analysis, we identified critical parietal and frontal contributions, along with EEG markers like elevated theta and reduced alpha power, commonly associated with Alzheimer’s disease. This method represents a significant step forward in developing explainable and high-performing EEG-based diagnostic tools. We will make the code and model’s weights publicly available upon publication at anonymized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".