Toward an EEG-Based Recognition of Music Liking Using Time-Frequency Analysis

• Published in: IEEE transactions on biomedical engineering Vol. 59; no. 12; pp. 3498 - 3510
• Main Authors: Hadjidimitriou, Stelios K., Hadjileontiadis, Leontios J.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Artificial Intelligence; Biological and medical sciences; Brain - physiology; Brain Mapping - methods; Brain models; Computer science; control theory; systems; Computerized, statistical medical data processing and models in biomedicine; Corruption; Electrodiagnosis. Electric activity recording; Electroencephalogram (EEG); Electroencephalography; Electroencephalography - methods; Exact sciences and technology; Feature extraction; Female; Frequency analysis; Humans; Investigative techniques, diagnostic techniques (general aspects); machine learning; Male; Medical management aid. Diagnosis aid; Medical sciences; Music; music liking/disliking; Nervous system; Pleasure - physiology; Signal Processing, Computer-Assisted; Support vector machines; Time frequency analysis; time-frequency (TF) analysis; Young Adult; Electrodiagnosis; time―frequency (TF) analysis; Electrophysiology; music liking/disliking; Electroencephalography; Electroencephalogram (EEG); machine learning; Artificial intelligence; Biomedical engineering
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2012.2217495

Affective phenomena, as reflected through brain activity, could constitute an effective index for the detection of music preference. In this vein, this paper focuses on the discrimination between subjects' electroencephalogram (EEG) responses to self-assessed liked or disliked music, acquired during an experimental procedure, by evaluating different feature extraction approaches and classifiers to this end. Feature extraction is based on time-frequency (TF) analysis by implementing three TF techniques, i.e., spectrogram, Zhao-Atlas-Marks distribution and Hilbert-Huang spectrum (HHS). Feature estimation also accounts for physiological parameters that relate to EEG frequency bands, reference states, time intervals, and hemispheric asymmetries. Classification is performed by employing four classifiers, i.e., support vector machines, k-nearest neighbors (k-NN), quadratic and Mahalanobis distance-based discriminant analyses. According to the experimental results across nine subjects, best classification accuracy {86.52 (±0.76)%} was achieved using k-NN and HHS-based feature vectors ( FVs) representing a bilateral average activity, referred to a resting period, in β (13-30 Hz) and γ (30-49 Hz) bands. Activity in these bands may point to a connection between music preference and emotional arousal phenomena. Furthermore, HHS-based FVs were found to be robust against noise corruption. The outcomes of this study provide early evidence and pave the way for the development of a generalized brain computer interface for music preference recognition.