Dataset-Independent EEG Channel Selection for Emotion Recognition
Bibliographic record
Abstract
Electroencephalography (EEG) stands as a noninvasive and cost-effective method for recording neural activity, holding potential for applications such as identifying neural processes underlying human emotions. This paper delves into the transferability and generalizability of EEG channel selection in emotion recognition, adopting a dataset-independent approach. By leveraging Power Spectral Density (PSD), we identify high-contributing EEG channels in the SEED V dataset and validate our approach on the independent SEED IV dataset using a Convolutional Neural Network (CNN) model. The channel selection method helped in eliminating insignificant EEG channels, which can improve the applicability of developing more efficient EEG devices for daily use to monitor emotions, as well as in individuals suffering from various neurodegenerative diseases. Through extensive experiments varying the number of channels and features, our model achieves classification accuracies of 77.02%, 75.42%, 71.31%, and 64.31% with 62, 30, 20, and 10 EEG channels, accompanied by 310, 90, 60, and 30 Differential Entropy (DE) features respectively. Further, the proposed approach is tested by introducing Gaussian noise to the training set and evaluating its sensitivity to signal noise. Finally, results are compared with state-of-the-art models highlighting the potential of our dataset-independent channel selection method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".