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Multi-Variate EEG Analysis as a Novel Tool to Examine Brain Responses to Naturalistic Music Stimuli.

Sturm I, Dähne S, Blankertz B, Curio G - PLoS ONE (2015)

Bottom Line: We demonstrate that a significant CACor (i) can be detected in an individual listener's EEG of a single presentation of a full-length complex naturalistic music stimulus, and (ii) it co-varies with the stimuli's average magnitudes of sharpness, spectral centroid, and rhythmic complexity.In particular, the subset of stimuli eliciting a strong CACor also produces strongly coordinated tension ratings obtained from an independent listener group in a separate behavioral experiment.Thus musical features that lead to a marked physiological reflection of tone onsets also contribute to perceived tension in music.

View Article: PubMed Central - PubMed

Affiliation: Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Berlin, Germany; Neurotechnology Group, Technische Universität Berlin, Berlin, Germany; Neurophysics Group, Department of Neurology, Charité University Medicine, Berlin, Germany.

ABSTRACT
Note onsets in music are acoustic landmarks providing auditory cues that underlie the perception of more complex phenomena such as beat, rhythm, and meter. For naturalistic ongoing sounds a detailed view on the neural representation of onset structure is hard to obtain, since, typically, stimulus-related EEG signatures are derived by averaging a high number of identical stimulus presentations. Here, we propose a novel multivariate regression-based method extracting onset-related brain responses from the ongoing EEG. We analyse EEG recordings of nine subjects who passively listened to stimuli from various sound categories encompassing simple tone sequences, full-length romantic piano pieces and natural (non-music) soundscapes. The regression approach reduces the 61-channel EEG to one time course optimally reflecting note onsets. The neural signatures derived by this procedure indeed resemble canonical onset-related ERPs, such as the N1-P2 complex. This EEG projection was then utilized to determine the Cortico-Acoustic Correlation (CACor), a measure of synchronization between EEG signal and stimulus. We demonstrate that a significant CACor (i) can be detected in an individual listener's EEG of a single presentation of a full-length complex naturalistic music stimulus, and (ii) it co-varies with the stimuli's average magnitudes of sharpness, spectral centroid, and rhythmic complexity. In particular, the subset of stimuli eliciting a strong CACor also produces strongly coordinated tension ratings obtained from an independent listener group in a separate behavioral experiment. Thus musical features that lead to a marked physiological reflection of tone onsets also contribute to perceived tension in music.

No MeSH data available.


Related in: MedlinePlus

EEG feature extraction.(1) In the first step of the analysis the 61-channel EEG signal (after generic preprocessing, see Methods) is temporally embedded and the power slope of the audio signal is extracted. In the training step (2) the embedded EEG features are regressed onto the audio power slope (Ridge Regression). After that (3) the resulting spatio-temporal filter (regression weight matrix) reducing the multichannel EEG to a one-dimensional projection is applied to a new presentation of the same stimulus. The regression filter can be transformed (4a) into a spatio-temporal pattern that indicates the distribution of information which is relevant for the reconstruction of the audio power slope. This spatio-temporal pattern, in turn, can be (4b) decomposed into components (derived with the MUSIC-algorithm) which have a scalp topography and a temporal signature. The EEG projections obtained in (3) subsequently are examined with respect to Cortico-Acoustic correlation (CACor).
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pone.0141281.g002: EEG feature extraction.(1) In the first step of the analysis the 61-channel EEG signal (after generic preprocessing, see Methods) is temporally embedded and the power slope of the audio signal is extracted. In the training step (2) the embedded EEG features are regressed onto the audio power slope (Ridge Regression). After that (3) the resulting spatio-temporal filter (regression weight matrix) reducing the multichannel EEG to a one-dimensional projection is applied to a new presentation of the same stimulus. The regression filter can be transformed (4a) into a spatio-temporal pattern that indicates the distribution of information which is relevant for the reconstruction of the audio power slope. This spatio-temporal pattern, in turn, can be (4b) decomposed into components (derived with the MUSIC-algorithm) which have a scalp topography and a temporal signature. The EEG projections obtained in (3) subsequently are examined with respect to Cortico-Acoustic correlation (CACor).

Mentions: The analysis aims to obtain a representation from the ongoing EEG that reflects brain responses to the onset ‘landscape’ of a music stimulus. It can be divided into four modules for (1) preprocessing EEG and audio data, (2) calculating spatio-temporal regression filters for optimally extracting EEG features, (3) applying the derived filters to new data in order to extract EEG projections, and (4) transforming the spatio-temporal filters into a representation suitable for neurophysiological interpretation. Finally, the synchronization between the extracted EEG projections in (3) and the audio stimulus is examined at several levels of temporal resolution and the results are related to behavioral results and stimulus characteristics. Fig 2 summarizes steps (1) to (4).


Multi-Variate EEG Analysis as a Novel Tool to Examine Brain Responses to Naturalistic Music Stimuli.

Sturm I, Dähne S, Blankertz B, Curio G - PLoS ONE (2015)

EEG feature extraction.(1) In the first step of the analysis the 61-channel EEG signal (after generic preprocessing, see Methods) is temporally embedded and the power slope of the audio signal is extracted. In the training step (2) the embedded EEG features are regressed onto the audio power slope (Ridge Regression). After that (3) the resulting spatio-temporal filter (regression weight matrix) reducing the multichannel EEG to a one-dimensional projection is applied to a new presentation of the same stimulus. The regression filter can be transformed (4a) into a spatio-temporal pattern that indicates the distribution of information which is relevant for the reconstruction of the audio power slope. This spatio-temporal pattern, in turn, can be (4b) decomposed into components (derived with the MUSIC-algorithm) which have a scalp topography and a temporal signature. The EEG projections obtained in (3) subsequently are examined with respect to Cortico-Acoustic correlation (CACor).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4624980&req=5

pone.0141281.g002: EEG feature extraction.(1) In the first step of the analysis the 61-channel EEG signal (after generic preprocessing, see Methods) is temporally embedded and the power slope of the audio signal is extracted. In the training step (2) the embedded EEG features are regressed onto the audio power slope (Ridge Regression). After that (3) the resulting spatio-temporal filter (regression weight matrix) reducing the multichannel EEG to a one-dimensional projection is applied to a new presentation of the same stimulus. The regression filter can be transformed (4a) into a spatio-temporal pattern that indicates the distribution of information which is relevant for the reconstruction of the audio power slope. This spatio-temporal pattern, in turn, can be (4b) decomposed into components (derived with the MUSIC-algorithm) which have a scalp topography and a temporal signature. The EEG projections obtained in (3) subsequently are examined with respect to Cortico-Acoustic correlation (CACor).
Mentions: The analysis aims to obtain a representation from the ongoing EEG that reflects brain responses to the onset ‘landscape’ of a music stimulus. It can be divided into four modules for (1) preprocessing EEG and audio data, (2) calculating spatio-temporal regression filters for optimally extracting EEG features, (3) applying the derived filters to new data in order to extract EEG projections, and (4) transforming the spatio-temporal filters into a representation suitable for neurophysiological interpretation. Finally, the synchronization between the extracted EEG projections in (3) and the audio stimulus is examined at several levels of temporal resolution and the results are related to behavioral results and stimulus characteristics. Fig 2 summarizes steps (1) to (4).

Bottom Line: We demonstrate that a significant CACor (i) can be detected in an individual listener's EEG of a single presentation of a full-length complex naturalistic music stimulus, and (ii) it co-varies with the stimuli's average magnitudes of sharpness, spectral centroid, and rhythmic complexity.In particular, the subset of stimuli eliciting a strong CACor also produces strongly coordinated tension ratings obtained from an independent listener group in a separate behavioral experiment.Thus musical features that lead to a marked physiological reflection of tone onsets also contribute to perceived tension in music.

View Article: PubMed Central - PubMed

Affiliation: Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Berlin, Germany; Neurotechnology Group, Technische Universität Berlin, Berlin, Germany; Neurophysics Group, Department of Neurology, Charité University Medicine, Berlin, Germany.

ABSTRACT
Note onsets in music are acoustic landmarks providing auditory cues that underlie the perception of more complex phenomena such as beat, rhythm, and meter. For naturalistic ongoing sounds a detailed view on the neural representation of onset structure is hard to obtain, since, typically, stimulus-related EEG signatures are derived by averaging a high number of identical stimulus presentations. Here, we propose a novel multivariate regression-based method extracting onset-related brain responses from the ongoing EEG. We analyse EEG recordings of nine subjects who passively listened to stimuli from various sound categories encompassing simple tone sequences, full-length romantic piano pieces and natural (non-music) soundscapes. The regression approach reduces the 61-channel EEG to one time course optimally reflecting note onsets. The neural signatures derived by this procedure indeed resemble canonical onset-related ERPs, such as the N1-P2 complex. This EEG projection was then utilized to determine the Cortico-Acoustic Correlation (CACor), a measure of synchronization between EEG signal and stimulus. We demonstrate that a significant CACor (i) can be detected in an individual listener's EEG of a single presentation of a full-length complex naturalistic music stimulus, and (ii) it co-varies with the stimuli's average magnitudes of sharpness, spectral centroid, and rhythmic complexity. In particular, the subset of stimuli eliciting a strong CACor also produces strongly coordinated tension ratings obtained from an independent listener group in a separate behavioral experiment. Thus musical features that lead to a marked physiological reflection of tone onsets also contribute to perceived tension in music.

No MeSH data available.


Related in: MedlinePlus