EEG Dataset for the Recognition of Different Emotions Induced in Voice-User Interaction.

ABSTRACT: Electroencephalography (EEG)-based open-access datasets are available for emotion recognition studies, where external auditory/visual stimuli are used to artificially evoke pre-defined emotions. In this study, we provide a novel EEG dataset containing the emotional information induced during a realistic human-computer interaction (HCI) using a voice user interface system that mimics natural human-to-human communication. To validate our dataset via neurophysiological investigation and binary emotion classification, we applied a series of signal processing and machine learning methods to the EEG data. The maximum classification accuracy ranged from 43.3% to 90.8% over 38 subjects and classification features could be interpreted neurophysiologically. Our EEG data could be used to develop a re

SUBMITTER: Choi GY 

PROVIDER: S-EPMC11449991 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature