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The Sound of Emotion: Pinpointing Emotional Voice Processing Via Frequency Tagging EEG
Tijdschriftbijdrage - Tijdschriftartikel
Successfully engaging in social communication requires efficient processing of subtle sociocommunicative
cues. Voices convey a wealth of social information, such as gender, identity, and the
emotional state of the speaker. We tested whether our brain can systematically and automatically
differentiate and track a periodic stream of emotional utterances among a series of neutral vocal
utterances. We recorded frequency-tagged EEG responses of 20 neurotypical male adults while
presenting streams of neutral utterances at a 4 Hz base rate, interleaved with emotional utterances
every third stimulus, hence at a 1.333 Hz oddball frequency. Four emotions (happy, sad, angry, and
fear) were presented as different conditions in different streams. To control the impact of low-level
acoustic cues, we maximized variability among the stimuli and included a control condition with
scrambled utterances. This scrambling preserves low-level acoustic characteristics but ensures that
the emotional character is no longer recognizable. Results revealed significant oddball EEG responses
for all conditions, indicating that every emotion category can be discriminated from the neutral
stimuli, and every emotional oddball response was significantly higher than the response for the
scrambled utterances. These findings demonstrate that emotion discrimination is fast, automatic, and
is not merely driven by low-level perceptual features. Eventually, here, we present a new database
for vocal emotion research with short emotional utterances (EVID) together with an innovative
frequency-tagging EEG paradigm for implicit vocal emotion discrimination.
Tijdschrift: Brain Sciences
ISSN: 2076-3425
Issue: 2
Volume: 13
Pagina's: 1 - 17
Jaar van publicatie:2023
Toegankelijkheid:Open