Prediction of Amusement Intensity Based on Brain Activity

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.