Emotions, Arousal, and Frontal Alpha Rhythm Asymmetry During Beethoven’s 5th Symphony

Abstract

Music is capable of inducing emotional arousal. While previous studies used brief musical excerpts to induce one specific emotion, the current study aimed to identify the physiological correlates of continuous changes in subjective emotional states while listening to a complete music piece. A total of 19 participants listened to the first movement of Ludwig van Beethoven's 5th symphony (duration: ~7.4 min), during which a continuous 76-channel EEG was recorded. In a second session, the subjects evaluated their emotional arousal during the listening. A fast fourier transform was performed and covariance maps of spectral power were computed in association with the subjective arousal ratings. Subjective arousal ratings had good inter-individual correlations. Covariance maps showed a right-frontal suppression of lower alpha-band activity during high arousal. The results indicate that music is a powerful arousal-modulating stimulus. The temporal dynamics of the piece are well suited for sequential analysis, and could be necessary in helping unfold the full emotional power of music.

Task Context Influences Brain Activation during Music Listening

“…These ratings can be made either verbally or along several dimensions (e.g., relaxation vs. arousal and/or sadness vs. happiness). The continuous rating of musical pieces is a relatively new strategy that has aided the identification of the time course of subjective experiences during music listening (Hutcherson et al, 2005; Mikutta et al, 2012, 2014; Jancke et al, 2015; Trost et al, 2015). For example, Mikutta et al (2014) instructed subjects to move a computer mouse forward when they experienced increased arousal caused by the music, independently of their affective valence.…”

“…As with Mikutta et al (2014), these ratings were recorded for offline analysis with a sampling rate of 100 Hz. These continuous ratings have been applied in three different ways so far: (1) continuous rating of the musical piece simultaneously during neurophysiological recording [Listening and Rating (LR)] (Hutcherson et al, 2005); (2) continuous rating during a second presentation of the musical piece, without neurophysiological recording (LR without neurophysiological recording in a second separate session) (Mikutta et al, 2012, 2013, 2014; Jancke et al, 2015; Trost et al, 2015); (3) continuous rating during the first presentation of the musical piece, without neurophysiological recording (LR without neurophysiological recording in an initial separate session).…”

“…The relative disadvantage of EEG recordings is the lower spatial resolution (if the aim is to estimate the underlying cortical sources of EEG activity), although the correspondence between EEG-based estimations of intracortical sources and fMRI measurements is astonishingly high (Britz et al, 2010; Van de Ville et al, 2010). Several published studies have used EEG to measure neural responses during music listening (Petsche et al, 1993; Iwaki et al, 1997; Sarnthein et al, 1997; Bhattacharya and Petsche, 2001; Bhattacharya et al, 2001a,b; Altenmuller et al, 2002; Jaušovec and Habe, 2005; Baumgartner et al, 2006; Jausovec et al, 2006; Peterson and Thaut, 2007; Sammler et al, 2007; Schaefer et al, 2009, 2011a,b, 2013; Mikutta et al, 2012, 2014; Wu et al, 2012; Jancke et al, 2015; Jancke and Alahmadi, 2016; Rogenmoser et al, 2016). Some of these studies focused on the functional network characteristics during music listening and identified specific network features in various frequency bands.…”

Aesthetic preference recognition of 3D shapes using EEG

“…The left DLPFC is activated during memorizing verbal stimuli while the right DLPFC is activated during memorizing the visual stimuli (Geuze et al 2008), this might explains channel F4 obtained an accuracy of 75 % using SVM with alpha, theta and delta rhythms, 77 % of accuracy using KNN with all rhythms, KNN with alpha, beta, theta and delta rhythms and SVM with alpha rhythm, and finally 79 % of accuracy using SVM with alpha and gamma rhythms, where the gamma rhythm is believed to play a part in memory encoding and retrieval (Jackson et al 2011). Meanwhile, suppression of low alpha activities at right frontal channel indicates high arousal (Mikutta et al 2012), which implies the preference. Nevertheless, the use of channels F3 and F4 with alpha and theta rhythms, and also alpha, beta and theta rhythms using SVM obtained an accuracy of 75 %.…”

Contrasting losses and gains increases the predictability of behavior by frontal EEG asymmetry

“…This hypothesis was confirmed by the finding that left frontal activity at rest is correlated with increased motivation to approach potentially positive stimuli (e.g., Davidson, 1992, 1995, 2004; Harmon-Jones and Allen, 1997; Sutton and Davidson, 1997, 2000; Coan et al, 2001; Coan and Allen, 2003, 2004) 1 and increased risk taking (Knoch et al, 2006; Gianotti et al, 2009; Studer et al, 2013). More recently it was found that frontal EEG asymmetry is only modestly heritable (Bismark et al, 2010; Lee et al, 2011) and that it is highly sensitive to external stimuli such as music (Mikutta et al, 2012). However, no previous study has examined boundary conditions for the behaviors that could be predicted by frontal asymmetry.…”