TFSNet: EEG-based Emotion Recognition using Temporal and Frequency-Spatial Feature
Bibliographic record
Abstract
Electroencephalography (EEG)-based Automatic Emotion Recognition (AER) has gained increasing attention as a reliable tool for affective computing. While prior studies have explored various temporal, frequency, and spatial-domain representations of EEG signals, few have effectively integrated these domains within a unified framework. In this paper, we propose TFSNet, a multi-domain deep learning model that combines temporal, frequency, and spatial features for robust emotion recognition. TFSNet consists of a dual-encoder architecture: a temporal encoder based on state-space modeling (S4D), and a frequency-spatial encoder that leverages CNNs, attention mechanisms, and graph filtering using a physiologically-informed adjacency matrix. These domain-specific embeddings are fused and passed through a classifier for final prediction. Experimental results on the DREAMER dataset demonstrate that TFSNet achieves superior performance across Valence, Arousal, and Dominance emotions, outperforming state-of-the-art models. The results highlight the effectiveness of combining domain-aware representations and spatial connectivity priors for EEG-based emotion recognition and its potential for real-time applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".