EEG-Based Prediction of Music Enjoyment Using Deep Learning with Explainable Temporal Analysis

A study posted to arXiv (2610.20795v1) reports that six deep learning models predicted music enjoyment from 128-channel EEG data on the public MUSIN-G dataset of 20 listeners, with DeepConvNet, CNN-LSTM and GNN-CNN achieving the lowest mean absolute error of 0.97 to 0.99 on a 1-to-5 scale under a participant-wise protocol using 15 listeners for training and validation and 5 held out for testing. The temporal convolutional network (TCN) showed the highest Pearson correlation with enjoyment ratings at r = 0.21, and gradient-based and occlusion-based explanations identified the time segments within each two-second window that most influenced TCN predictions. The authors present the work as a reproducible cross-listener benchmark for EEG-based enjoyment regression.

arXiv:2610.20795v1 Announce Type: new Abstract: Understanding why listeners enjoy music is difficult because the experience is subjective and changes as a piece unfolds. Electroencephalography EEG offers a non-invasive way to study neural responses during listening, yet earlier work has mostly treated music emotion as a categorical label or relied on black-box models. We formulate enjoyment prediction as a regression problem on the public MUSIN-G dataset, which contains 128-channel EEG from 20 listeners together with song-level enjoyment ratings. Six deep learning models were compared under a participant-wise protocol, in which 15 listeners were used for training and validation and 5 were held out for testing. DeepConvNet, CNN-LSTM and GNN-CNN reached the lowest mean absolute error 0.97 to 0.99 on the 1 to 5 scale , while the temporal convolutional network TCN had the highest Pearson correlation with the ratings r = 0.21 . Gradient-based and occlusion-based explanations, averaged over all held-out windows, identify the time segments within each two-second window that most influence the TCN predictions. The study provides a reproducible cross-listener benchmark for EEG-based enjoyment regression and shows how explainability analysis complements standard metrics.