Mutual information between inter-hemispheric EEG spectro-temporal patterns: A new feature for automated affect recognition
Bibliographic record
Abstract
Automated electroencephalography (EEG) based affect recognition has gained a lot of interest recently, with clinical (e.g., in autism), human-computer interaction (e.g., affective brain-computer interfaces), neuromarketing, and even multimedia (e.g., affective video tagging) applications. Typically, conventional EEG features such as spectral power, coherence, and frontal asymmetry have been used to characterize affective states. Recently, cross-frequency coupling measures have also been explored. In this paper, we propose a new feature set that combines some of these aforementioned paradigms. First, the full-band EEG signal is decomposed into four subband signals, namely theta, alpha, beta, and gamma. The amplitude modulation (or envelope) of these signals is then computed via a Hilbert transform. These amplitude modulations are further decomposed into 10 cross-frequency coupling patterns (e.g., gamma-beta coupling pattern). The mutual information between each of these ten patterns is then calculated for all inter-hemispheric EEG electrode pairs. To gauge the effectiveness of the newly-proposed feature set, the so-called DEAP database was used. Experimental results show the proposed feature set outperforming conventional ones for estimation of arousal, valence, dominance, and liking affective dimensions. Gains of up to 20% could be achieved when the proposed features were fused with spectral power and asymmetry index features, thus suggesting complementarity between spectral and spectro-temporal features for automated affective state recognition.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".