{"slug": "eeg-based-prediction-of-music-enjoyment-using-deep-learning-with-explainable", "title": "EEG-Based Prediction of Music Enjoyment Using Deep Learning with Explainable Temporal Analysis", "summary": "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.", "body_md": "arXiv:2610.20795v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/eeg-based-prediction-of-music-enjoyment-using-deep-learning-with-explainable", "canonical_source": "https://arxiv.org/abs/2610.20795", "published_at": "2026-10-16 04:00:00+00:00", "updated_at": "2026-10-16 04:32:53.455732+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "ai-research"], "entities": ["arXiv", "MUSIN-G", "DeepConvNet", "CNN-LSTM", "GNN-CNN", "TCN"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/eeg-based-prediction-of-music-enjoyment-using-deep-learning-with-explainable", "markdown": "https://wpnews.pro/news/eeg-based-prediction-of-music-enjoyment-using-deep-learning-with-explainable.md", "text": "https://wpnews.pro/news/eeg-based-prediction-of-music-enjoyment-using-deep-learning-with-explainable.txt", "jsonld": "https://wpnews.pro/news/eeg-based-prediction-of-music-enjoyment-using-deep-learning-with-explainable.jsonld"}}