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.
Figures & tables
Figure 1: EEG preprocessing and analysis pipeline, showing average referencing, high-pass filtering, ICA-based artifact rejection, downsampling, channel interpolation, segmentation, and participant-wise data splitting
Parameter
Value / Description
Batch size
16
Initial learning rate
3×10−4
Optimizer
Adam / AdamW
LR scheduler
CosineAnnealing
Dropout rate
0.5–0.6
Maximum epochs
50
Table 1: Training hyperparameters applied consistently across all models
Metric
Direction
Description
MAE
Lower is better
Avg. absolute deviation
RMSE
Lower is better
Penalizes large errors
Pearson r
Higher is better
Trend alignment
Tol. Accuracy
Higher is better
Predictions within ±1
Table 2: Evaluation metrics and their purpose in enjoyment regression
Model
Spatial Modeling
Temporal Modeling
Complexity
Core Strength
Limitation
EEGNet
Depthwise filters
Short-range convolutions
Low
Parameter-efficient
Limited context
ShallowConvNet
Shallow convolutions
Band-power filtering
Low–Med
Interpretable features
Insufficient depth
DeepConvNet
Hierarchical features
Multi-scale blocks
High
Strong accuracy
Inter-subject variability
CNN–LSTM
CNN-based encoder
Recurrent modeling
High
Sequential dynamics
Training instability
TCN
Implicit integration
Dilated causal convs
Med–High
Best trend alignment
Slightly higher MAE
GNN–CNN
Graph connectivity
Temporal graph convs
High
Explicit relationships
Reduced generalization
Table 3: Architectural trade-offs and performance characteristics of evaluated deep learning models
Model
MAE ↓
RMSE ↓
Pearson r↑
Tol. Acc. (%) ↑
EEGNet
1.03
1.27
−0.01
46.7
ShallowConvNet
1.31
1.56
−0.06
35.0
DeepConvNet
0.99
1.24
0.20
51.7
CNN–LSTM
0.98
1.18
0.18
56.7
TCN
1.05
1.28
0.21
55.0
GNN–CNN
0.97
1.19
0.18
58.3
Table 4: Regression performance on the MUSIN-G dataset (15-train / 5-test participant-wise split)
Criterion
Best Model
Observation
Lowest MAE
GNN–CNN
Best absolute accuracy
Lowest RMSE
CNN–LSTM
Best against large errors
Highest Pearson r
TCN
Best trend alignment
Highest Tol. Acc.
GNN–CNN
Best proximity
Table 5: Best-performing model per evaluation criterion
Figure 2: Grad-CAM visualization highlighting temporally salient EEG regions for enjoyment prediction. Relevance is highest at the start of the window and decays over time
Figure 3: Input gradient saliency map illustrating sensitivity of TCN predictions to localized EEG signal variations across time.
Figure 4: Occlusion-based explainability analysis showing the contribution of individual temporal EEG windows to enjoyment prediction
Figure 5: Summary of XAI analysis. Temporal relevance is distributed across sustained EEG segments rather than isolated events, supporting a context-driven interpretation of enjoyment prediction