Multimodal Deep Learning Model for Subject-Independent EEG-based Emotion Recognition
Bibliographic record
Abstract
Regulating emotion is crucial for maintaining well-being and social relationships. However, as we age, the volume of the frontal lobes is reduced, which can cause difficulties in regulating emotions. Electroencephalography (EEG)-based emotion recognition has the potential to understand the complexity of human emotions and the atrophy of the frontal lobes that leads to cognitive impairment. In this study, we investigated a multimodal deep learning approach for subject-independent emotion recognition using EEG and eye movement data. To that end, we proposed an attention mechanism layer to fuse features extracted from the EEG and eye movement data. We tested our approach in two benchmarking emotion recognition datasets: SEED-IV and SEED-V. Our approach achieved an average accuracy of 67.3% and 72.3% for SEED-IV and SEED-V, respectively. Our results demonstrate the potential of multimodal deep learning models for subject-independent emotion recognition using EEG and eye movement data, which can have important implications for assessing emotional regulation in clinical and research settings.
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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 | 0.000 |
| 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) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".