Type: dissertation
Abstract: Emotion Recognition is a process that uses affective models and different measurement methods to identify behavioral states in the human body and label them as emotions or affective states.Brain Computer Interphases (BCIs) have adopted the use of emotions recognition under the domain of affective BCIs, which attempt to create systems able to detect affective states from neurophysiological signals, to enhance the human-computer interaction (HCI).The purpose of this research is to Identify emotional reactions using both electroencephalography (EEG) signals and participants' individual characteristics under HCI scenarios.EEG traits and individual characteristics were analyzed to identify which ones are useful in the emotion classification process (represented as arousal and valence labels) from public emotion recognition dataset.Then, a videogame to elicit different emotions, while recording EEG signals, self-assessment emotional answers, and behavioral cues was designed, to analyze the correlation, and classification between EEG traits, arousal-valence responses and videogame time events as behavioral cues related with emotional reactions.I would like to show greatest appreciation to my advisor
Author: Laura Alejandra Martinez Tejada
Language: English
Topic: Emotion and Mood Recognition
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7223620050