Linking brain responses to naturalistic music through analysis of ongoing EEG and stimulus features

Cong, Fengyu, Alluri, Vinoo, Nandi, Asoke K, Toiviainen, Petri, Fa, Rui, Abu-Jamous, Basel, Gong, Liyun, G.W.Craenen, Bart, Poikonen, Hanna, Huotilainen, Minna and Ristaniemi, Tapani (2013) Linking brain responses to naturalistic music through analysis of ongoing EEG and stimulus features. IEEE Transactions on Multimedia, 15 (5). pp. 1060-1069. ISSN 1520-9210

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Abstract

This study proposes a novel approach for the analysis of brain responses in the modality of ongoing EEG elicited
by the naturalistic and continuous music stimulus. The 512-second long EEG data (recorded with 64 electrodes) are first decomposed into 64 components by independent component analysis (ICA) for each participant. Then, the spatial maps showing dipolar brain activity are selected in terms of the residual dipole variance through a single dipole model in brain imaging, and clustered into a pre-defined number (estimated by the minimum description length) of clusters. Subsequently, the temporal courses of the EEG theta and alpha oscillations of each component for each cluster are produced and correlated with the temporal courses of tonal and rhythmic features of the music. Using this approach, we found that the extracted temporal courses of the theta and alpha oscillations along central and occipital area of scalp in two of the selected clusters significantly correlated with the musical features representing progressions
in the rhythmic content of the stimulus. We suggest that this demonstrates that with the proposed approach, we have managed to discover what kinds of brain responses were elicited when a participant was listening continuously to the long piece of naturalistic music.

Keywords:clustering, independent component analysis, natural continuous music, ongoing, oscillation.
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Computer Science
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ID Code:28869
Deposited On:03 Oct 2017 08:42

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