Prediction of Amusement Intensity Based on Brain Activity
Bibliographic record
Abstract
Amusement can help modulate psychological disorders and cognitive functions. Unfortunately, algorithms classifying emotions still combine multiple positive emotions into a unique emotion, namely joy, making it hard to use amusement in a real-life setting. Here we train a Long-Short-Term-Memory (LSTM) on electroencephalography (EEG) to predict amusement on a categorical scale. Participants (n=10) watched and rated 120 videos with various funniness levels while their brain activity was recorded with an Emotiv Headset. Participant’s ratings were divided into four bins of amusement (low, medium, high & very high) based on the participant’s ranking’s percentile. Nested cross-validation was used to validate the models. We first left out one video from each participant for the final model’s validation and a leave-one-group-out technique was used to test the model on an unseen participant during the training phase. The nested cross-validation was tested on sixteen different videos. We created an LSTM model with five hidden layers, vatch size of 256 and an input layer of 14 x 128 (number of electrodes x 1 sec of recording) and four nodes representing the different levels of amusement. The best model obtained during the training phase was tested on the unseen video. While the level of accuracy between the validation videos varies slightly (mean=57.3%, std=13.7%), our best model obtained an accuracy of 82,4%. This high accuracy supports the use of brain activity to predict amusement. Moreover, the validation process we design conveys that models using this technique are transferable across participants and videos.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".