DocumentCode :
1764453
Title :
Linking Brain Responses to Naturalistic Music Through Analysis of Ongoing EEG and Stimulus Features
Author :
Fengyu Cong ; Alluri, Vinoo ; Nandi, A.K. ; Toiviainen, Petri ; Rui Fa ; Abu-Jamous, Basel ; Liyun Gong ; Craenen, B.G.W. ; Poikonen, Hanna ; Huotilainen, M. ; Ristaniemi, T.
Author_Institution :
Dept. of Math. Inf. Technol., Univ. of Jyvaskya, Jyväskyä, Finland
Volume :
15
Issue :
5
fYear :
2013
fDate :
Aug. 2013
Firstpage :
1060
Lastpage :
1069
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 :
electroencephalography; independent component analysis; medical signal processing; signal classification; EEG alpha oscillations; EEG theta oscillations; ICA; brain imaging; brain response analysis; brain response linking; continuous music stimulus; dipolar brain activity; independent component analysis; naturalistic music stimulus; ongoing EEG analysis; residual dipole variance; rhythmic features; stimulus features; tonal features; Brain; Educational institutions; Electrodes; Electroencephalography; Imaging; Music; Oscillators; Acoustical features; EEG; clustering; independent component analysis; natural continuous music; ongoing; oscillation;
fLanguage :
English
Journal_Title :
Multimedia, IEEE Transactions on
Publisher :
ieee
ISSN :
1520-9210
Type :
jour
DOI :
10.1109/TMM.2013.2253452
Filename :
6482644
Link To Document :
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