Multi-Modal Emotion Recognition Using EEG and Eye Tracking Features
Bibliographic record
Abstract
Multi-modal emotion recognition from various human physiological indicators has emerged as a large topic of interest, including the use of EEG, ECG, GSR and Eye Tracking features. This work introduced a simple CNN based multi-modal EEG and Eye Tracking emotion recognition model for the SEED V dataset. In contrast to other works on the SEED V dataset, different Differential Entropy time windows were tested for EEG feature extraction. EEG signals were arranged in a 2D image format to preserve spatial relationships between electrode placements on patients during the trials. The proposed model with a 1 second processing window for EEG features achieved state of the art results in Leave One Subject Out Validation, with a mean accuracy of 0.935 ± 0.038 on the SEED V dataset. A noticeable improvement was noted over the same multi-modal model using a 4 second processing window for EEG features, highlighting the importance of smaller time windows for EEG feature processing in emotion recognition problems.
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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 | 0.000 |
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".