6533b871fe1ef96bd12d1a2c

RESEARCH PRODUCT

Exploiting ongoing EEG with multilinear partial least squares during free-listening to music

Minna HuotilainenDeqing WangFengyu CongTapani RistaniemiAsoke K. NandiQibin ZhaoAndrzej CichockiPetri Toiviainen

subject

ta113Multilinear mapmedicine.diagnostic_testBrain activity and meditationSpeech recognition02 engineering and technologyElectroencephalographyta3112Matrix decomposition03 medical and health sciences0302 clinical medicinetensor decompositionFrequency domainPartial least squares regression0202 electrical engineering electronic engineering information engineeringmedicineSpectrogramOngoing EEG020201 artificial intelligence & image processingmusicTime domain030217 neurology & neurosurgerymultilinear partial least squaresMathematics

description

During real-world experiences, determining the stimulus-relevant brain activity is excitingly attractive and is very challenging, particularly in electroencephalography. Here, spectrograms of ongoing electroencephalogram (EEG) of one participant constructed a third-order tensor with three factors of time, frequency and space; and the stimulus data consisting of acoustical features derived from the naturalistic and continuous music formulated a matrix with two factors of time and the number of features. Thus, the multilinear partial least squares (PLS) conforming to the canonical polyadic (CP) model was performed on the tensor and the matrix for decomposing the ongoing EEG. Consequently, we found that brain activity of majority of participants was significantly correlated with the musical features in time domain, and that such brain activity showed frontal or central or posterior or occipital distributions along the scalp, and that such brain activity could be of different oscillation bands in frequency domain.

yearjournalcountryeditionlanguage
2016-09-01
10.1109/mlsp.2016.7738849https://doi.org/10.1109/MLSP.2016.7738849