Emotion recognition based on correlation between left and right frontal EEG assymetry
Bibliographic record
Abstract
The significant role of emotion recognition research has increased in last few years of human daily life. In this paper, we base on electroencephalogram (EEG) of the brain to recognize the internal emotion of participants. We use arousal and valence emotion elicitation process to calculate multidimensional direct information (MDI) between right and left hemisphere. The main contribution is recognizing internal emotion of people based on reading the signals from frontal asymmetry of brain so that four channels are used to represent frontal asymmetry of brain F3, F7, F4 and F8. The emotion elicitation process is focused on frontal EEG asymmetry base on using standard dataset. We test four basic emotions happy, sad, anger and relax. These emotions represent high arousal high valence, low arousal low valance, High arousal low valence and low arousal high valence in arousal and valance model. Statistical-based features from EEG signals of data inputs are extracted and used to calculate multidimensional direct information in order to find the correlation between left and right hemisphere to each emotion.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; both teacher heads agree on what is shown here.
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".