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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".