Musical groove modulates motor cortex excitability: A TMS investigation

Stupacher, Jan; Hove, Michael J.; Novembre, Giacomo; Schütz‐Bosbach, Simone; Keller, Peter E.

doi:10.1016/j.bandc.2013.03.003

Cited by 166 publications

(212 citation statements)

References 65 publications

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“…This observation supports previous suggestions that the ease of sensorimotor coupling with musical stimuli underlies perceived and experienced groove (Fairhurst, Janata, & Keller, 2013;Janata et al, 2012;Stupacher et al, 2013).…”

Section: Resultssupporting

confidence: 91%

“…Beat salience (a measure of rhythmic periodicity based on the autocorrelation function of the signal representing the velocities of event onsets) and event density (a measure of the variability in the event onset velocity signal) have been identified as the best predictors of perceived groove across a range of genres (Madison et al, 2011). However, when calculated using the Music Information Retrieval Toolbox (MIR Toolbox) for MATLAB (Lartillot & Toiviainen, 2007), event density failed to predict groove ratings (Stupacher et al, 2013). In sum, MIR analyses suggest that spectral features (especially low frequency spectral flux) are linked to movement qualities, and that event density and beat salience may underlie groove ratings.…”

Section: Audio Features Of Groovementioning

confidence: 99%

“…The features were selected based on the results of previous studies that examined relations between acoustic descriptors and subjective ratings (Madison et al, 2011;Stupacher et al, 2013), and relations between acoustic descriptors and music-induced movement (Burger et al, 2012; Audio Features of Groove 7…”

Section: Studymentioning

confidence: 99%

“…We selected the following audio features from the MIR Toolbox (Lartillot & Toiviainen, 2007) that were identified as related to music-induced movement or groove in previous studies: Spectral flux, sub-band flux (Alluri & Toiviainen, 2009;Burger et al, 2012;Stupacher et al, 2013), measures of rhythmic clarity (pulse clarity in Burger et al, 2012; beat salience in Madison et al, 2011), event density (Madison et al, 2011), RMS energy (Janata & Tomic, unpublished data), along with a derived measure of the variance in RMS energy. Given the common assumption that RMS is primarily a measure of loudness, we also computed a measure of loudness following the model of Glasberg and Moore (2002).…”

Section: Studymentioning

confidence: 99%

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Audio Features Underlying Perceived Groove and Sensorimotor Synchronization in Music

2016

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The experience of groove is associated with the urge to move to a musical rhythm. Here we focus on the relevance of audio features, obtained using music information retrieval (MIR) tools, for explaining the perception of groove and music-related movement. In Study 1 we extracted audio features from clips of real music previously rated on perceived groove. Measures of variability, such as the variance of the audio signal’s RMS curve and spectral flux (particularly in low frequencies), predicted groove ratings. Additionally, we dissociated two forms of event density, showing that an algorithm that emphasizes variability between beats predicted groove ratings better. In Study 2 we manipulated RMS levels and groove category (low, mid, and high groove) to confirm that perceived groove is not a function of loudness. In Study 3 we utilized novel music clips that manipulated the frequency of bass and bass drum (low vs. high) and attack time (short vs. long). Groove ratings and tapping velocities tended to be higher and tapping variability tended to be lower when the bass instruments had lower frequencies. The present findings emphasize the multifaceted nature of groove by linking audio and musical qualities to subjective experience and motor behavior.

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Section: Resultssupporting

confidence: 91%

Section: Audio Features Of Groovementioning

confidence: 99%

Section: Studymentioning

confidence: 99%

Section: Studymentioning

confidence: 99%

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Audio Features Underlying Perceived Groove and Sensorimotor Synchronization in Music

2016

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“…It was also shown that naive subjects, after a short musical training, had increased sensorimotor co-activation during passive listening of trained pieces [25,26], and passive listening of a trained piece induces a specific corticospinal facilitation after 30 minutes of practice [27]. Interestingly, rhythmic complexity or music syncopation is an important structural factor in embodied and affective responses to musical groove [28], and, indeed, high-groove music increasingly engages the motor system in musicians [29]. In addition, the observation of a mute piano fingering error induces a somatotopic time-locked corticospinal excitability modulation [30].…”

Section: Mirror Network and Musicmentioning

confidence: 98%

Measuring social interaction in music ensembles

Volpe

D’Ausilio

Badino

et al. 2016

Phil. Trans. R. Soc. B

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One contribution of 15 to a theme issue 'Attending to and neglecting people'. Music ensembles are an ideal test-bed for quantitative analysis of social interaction. Music is an inherently social activity, and music ensembles offer a broad variety of scenarios which are particularly suitable for investigation. Small ensembles, such as string quartets, are deemed a significant example of self-managed teams, where all musicians contribute equally to a task. In bigger ensembles, such as orchestras, the relationship between a leader (the conductor) and a group of followers (the musicians) clearly emerges. This paper presents an overview of recent research on social interaction in music ensembles with a particular focus on (i) studies from cognitive neuroscience; and (ii) studies adopting a computational approach for carrying out automatic quantitative analysis of ensemble music performances.

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Experimental suppression of transcranial magnetic stimulation‐electroencephalography sensory potentials

Ross

Sarkar

Keller

2022

Human Brain Mapping

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The sensory experience of transcranial magnetic stimulation (TMS) evokes cortical responses measured in electroencephalography (EEG) that confound interpretation of TMS-evoked potentials (TEPs). Methods for sensory masking have been proposed to minimize sensory contributions to the TEP, but the most effective combination for suprathreshold TMS to dorsolateral prefrontal cortex (dlPFC) is unknown. We applied sensory suppression techniques and quantified electrophysiology and perception from suprathreshold dlPFC TMS to identify the best combination to minimize the sensory TEP. In 21 healthy adults, we applied single pulse TMS at 120% resting motor threshold (rMT) to the left dlPFC and compared EEG vertex N100-P200 and perception. Conditions included three protocols: No masking (no auditory masking, no foam, and jittered interstimulus interval [ISI]), Standard masking (auditory noise, foam, and jittered ISI), and our ATTENUATE protocol (auditory noise, foam, over-the-ear protection, and unjittered ISI). ATTENUATE reduced vertex N100-P200 by 56%, "click" loudness perception by 50%, and scalp sensation by 36%. We show that sensory prediction, induced with predictable ISI, has a suppressive effect on vertex N100-P200, and that combining standard suppression protocols with sensory prediction provides the best N100-P200 suppression. ATTENUATE was more effective than Standard masking, which only reduced vertex N100-P200 by 22%, loudness by 27%, and scalp sensation by 24%. We introduce a sensory suppression protocol superior to Standard masking and demonstrate that using an unjittered ISI can contribute to minimizing sensory confounds. ATTENUATE provides superior sensory suppression to increase TEP signal-to-noise and contributes to a growing understanding of TMS-EEG sensory neuroscience.

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Musical groove modulates motor cortex excitability: A TMS investigation

Cited by 166 publications

References 65 publications

Audio Features Underlying Perceived Groove and Sensorimotor Synchronization in Music

Audio Features Underlying Perceived Groove and Sensorimotor Synchronization in Music

Measuring social interaction in music ensembles

Experimental suppression of transcranial magnetic stimulation‐electroencephalography sensory potentials

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