EEG-based cross-subject passive music pitch perception using deep learning models

• Published in: Cognitive neurodynamics Vol. 19; no. 1; p. 6
• Main Authors: Meng, Qiang, Tian, Lan, Liu, Guoyang, Zhang, Xue
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.12.2025; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Auditory perception; Biochemistry; Biomedical and Life Sciences; Biomedicine; Brain; Classification; Cognitive Psychology; Computer Science; Data collection; Deep learning; EEG; Electrodes; Electroencephalography; Experiments; Frequency; Go/no-go discrimination learning; Machine learning; Modelling; Music; Neural networks; Neurosciences; Optimization; Perception; Questionnaires; Research Article; Signal processing; Temporal perception; Windows (intervals); Objective evaluation; Electroencephalography; Cross -subject classification; Passive pitch perception
• ISSN: 1871-4080; 1871-4099
• DOI: 10.1007/s11571-024-10196-9

Pitch plays an essential role in music perception and forms the fundamental component of melodic interpretation. However, objectively detecting and decoding brain responses to musical pitch perception across subjects remains to be explored. In this study, we employed electroencephalography (EEG) as an objective measure to obtain the neural responses of musical pitch perception. The EEG signals from 34 subjects under hearing violin sounds at pitches G3 and B6 were collected with an efficient passive Go/No-Go paradigm. The lightweight modified EEGNet model was proposed for EEG-based pitch classification. Specifically, within-subject modeling with the modified EEGNet model was performed to construct individually optimized models. Subsequently, based on the within-subject model pool, a classifier ensemble (CE) method was adopted to construct the cross-subject model. Additionally, we analyzed the optimal time window of brain decoding for pitch perception in the EEG data and discussed the interpretability of these models. The experiment results show that the modified EEGNet model achieved an average classification accuracy of 77% for within-subject modeling, significantly outperforming other compared methods. Meanwhile, the proposed CE method achieved an average accuracy of 74% for cross-subject modeling, significantly exceeding the chance-level accuracy of 50%. Furthermore, we found that the optimal EEG data window for the pitch perception lies 0.4 to 0.9 s onset. These promising results demonstrate that the proposed methods can be effectively used in the objective assessment of pitch perception and have generalization ability in cross-subject modeling.