Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening

Yongjie Zhu, Chi Zhang, Petri Toiviainen, Minna Johanna Huotilainen, Klaus Mathiak, Tapani Ristaniemi, Fengyu Cong

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinen

Kuvaus

Recently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method that combined music information retrieval with spatial Independent Components Analysis (ICA) to probe the interplay between the spatial profiles and the spectral patterns. We projected the sensor data into cortical space using a minimum-norm estimate and applied the Short Time Fourier Transform (STFT) to obtain frequency information. Then, spatial ICA was made to extract spatial-spectral-temporal information of brain activity in source space and five long-term musical features were computationally extracted from the naturalistic stimuli. The spatial profiles of the components whose temporal courses were significantly correlated with musical feature time series were clustered to identify reproducible brain networks across the participants. Using the proposed approach, we found brain networks of musical feature processing are frequency-dependent and three plausible frequency-dependent networks were identified; the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.
Alkuperäiskielienglanti
LehtibioRxiv : the preprint server for biology
TilaJulkaistu - 2019
OKM-julkaisutyyppiB1 Kirjoitus tieteellisessä aikakauslehdessä

Lainaa tätä

Zhu, Yongjie ; Zhang, Chi ; Toiviainen, Petri ; Huotilainen, Minna Johanna ; Mathiak, Klaus ; Ristaniemi, Tapani ; Cong, Fengyu. / Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening. Julkaisussa: bioRxiv : the preprint server for biology . 2019.
@article{1bf89da6e7834aa89b5390ce72e2c818,
title = "Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening",
abstract = "Recently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method that combined music information retrieval with spatial Independent Components Analysis (ICA) to probe the interplay between the spatial profiles and the spectral patterns. We projected the sensor data into cortical space using a minimum-norm estimate and applied the Short Time Fourier Transform (STFT) to obtain frequency information. Then, spatial ICA was made to extract spatial-spectral-temporal information of brain activity in source space and five long-term musical features were computationally extracted from the naturalistic stimuli. The spatial profiles of the components whose temporal courses were significantly correlated with musical feature time series were clustered to identify reproducible brain networks across the participants. Using the proposed approach, we found brain networks of musical feature processing are frequency-dependent and three plausible frequency-dependent networks were identified; the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.",
author = "Yongjie Zhu and Chi Zhang and Petri Toiviainen and Huotilainen, {Minna Johanna} and Klaus Mathiak and Tapani Ristaniemi and Fengyu Cong",
year = "2019",
language = "English",
journal = "bioRxiv : the preprint server for biology",
publisher = "Cold Spring Harbor Laboratory",

}

Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening. / Zhu, Yongjie; Zhang, Chi; Toiviainen, Petri; Huotilainen, Minna Johanna; Mathiak, Klaus; Ristaniemi, Tapani; Cong, Fengyu.

julkaisussa: bioRxiv : the preprint server for biology , 2019.

Tutkimustuotos: ArtikkelijulkaisuArtikkeliTieteellinen

TY - JOUR

T1 - Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening

AU - Zhu, Yongjie

AU - Zhang, Chi

AU - Toiviainen, Petri

AU - Huotilainen, Minna Johanna

AU - Mathiak, Klaus

AU - Ristaniemi, Tapani

AU - Cong, Fengyu

PY - 2019

Y1 - 2019

N2 - Recently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method that combined music information retrieval with spatial Independent Components Analysis (ICA) to probe the interplay between the spatial profiles and the spectral patterns. We projected the sensor data into cortical space using a minimum-norm estimate and applied the Short Time Fourier Transform (STFT) to obtain frequency information. Then, spatial ICA was made to extract spatial-spectral-temporal information of brain activity in source space and five long-term musical features were computationally extracted from the naturalistic stimuli. The spatial profiles of the components whose temporal courses were significantly correlated with musical feature time series were clustered to identify reproducible brain networks across the participants. Using the proposed approach, we found brain networks of musical feature processing are frequency-dependent and three plausible frequency-dependent networks were identified; the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.

AB - Recently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method that combined music information retrieval with spatial Independent Components Analysis (ICA) to probe the interplay between the spatial profiles and the spectral patterns. We projected the sensor data into cortical space using a minimum-norm estimate and applied the Short Time Fourier Transform (STFT) to obtain frequency information. Then, spatial ICA was made to extract spatial-spectral-temporal information of brain activity in source space and five long-term musical features were computationally extracted from the naturalistic stimuli. The spatial profiles of the components whose temporal courses were significantly correlated with musical feature time series were clustered to identify reproducible brain networks across the participants. Using the proposed approach, we found brain networks of musical feature processing are frequency-dependent and three plausible frequency-dependent networks were identified; the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.

M3 - Article

JO - bioRxiv : the preprint server for biology

JF - bioRxiv : the preprint server for biology

ER -