Emotion Classification from Electroencephalogram Signals Using a Cascade of Convolutional and Block-Based Residual Recurrent Neural Networks
Bibliographic record
Abstract
To determine the quality of experience for users of technological devices, we must consider the human influential factors, which encompass the emotional state. Hence, we propose a model to estimate user emotions from Electroencephalogram (EEG) signals. The model is a cascade of deep learning networks consisting of a pre-trained convolutional neural network which extracts spatial relations and residual block(s) of recurrent neural network which learn the temporal relations of multi-channel EEG signals and uses shortcuts across the neural layers for a more effective training of the deep network. We adopted the DEAP dataset to train and evaluate our model. To confirm that the proposed work is user-independent, we ensure that the data in the test set corresponds to subjects that are not included in the training set. We explored several input sets to determine the one that performs best on the DEAP dataset. We implemented existing popular state-of-the-art methods and compared with the proposed model. The results indicate that the proposed model consistently outperforms the previous models achieving 0.61 and 0.63 accuracy on the validation and 0.65 and 0.68 accuracy on the test dataset for valence and arousal respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".