2018
DOI: 10.1016/j.biopsycho.2018.10.003
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Affect dynamics of facial EMG during continuous emotional experiences

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Cited by 40 publications
(32 citation statements)
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“…For each signal, the absolute values plus one was natural log-transformed to correct for the right-skewness of the raw data distribution. This transformation is commonly applied to EMG data to reduce the impact of extreme values 13,[52][53][54][55] . The log-transformed data were used in the main text to show results in a manner comparable to the approach used in previous studies.…”
Section: Methodsmentioning
confidence: 99%
“…For each signal, the absolute values plus one was natural log-transformed to correct for the right-skewness of the raw data distribution. This transformation is commonly applied to EMG data to reduce the impact of extreme values 13,[52][53][54][55] . The log-transformed data were used in the main text to show results in a manner comparable to the approach used in previous studies.…”
Section: Methodsmentioning
confidence: 99%
“…While it is less clear whether the zygomaticus major (smiling) muscle is sensitive to bipolar valence (Larson et al, 2003) it is sensitive to positive affect with increased activation for positive stimuli (Larsen et al, 2003;Williams, Leong, Collier, & Zaki, 2019;Winkielman & Cacioppo, 2001). For instance, a recent study provided evidence that zygomaticus activity during presentation of positive (but not negative) movie scenes was highly correlated with subjective ratings of participants, but also closely tracked dynamic changes of positive affect in the movies over time (Golland, Hakim, Aloni, Schaefer, & Levit-Binnun, 2018). Interestingly, when performing a simple response-interference task (and without any affective stimuli presented), Lindström and colleagues showed stronger EMG over the corrugator supercilii muscle within 200 ms following an error relative to a correct trial (Lindström, Mattsson-Mårn, Golkar, & Olsson, 2013).…”
Section: Highlightsmentioning
confidence: 99%
“…The EMG signal was bandpass filtered at 20-400 Hz. A fast Fourier transform with a 1 s Hanning window and 0.5 s overlap was applied to filtered data in order to calculate power spectral density estimates (van Reekum et al 2010;Lapate et al 2014;Golland et al 2018). The estimates were averaged and z-transformed to take account of variations in amplitudes between subjects.…”
Section: Electromyography Preprocessingmentioning
confidence: 99%