We present eSEEd- emotional State Estimation based on Eye-tracking database. Eye movements of 48 participants were recorded as they watched 10 emotion evoking videos each of them followed by a neutral video. Participants rated five emotions (tenderness, anger, disgust, sadness, neutral) on a scale from 0 to 10, later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled 3 self-assessment questionnaires. An extensive analysis of the participants' answers to the questionnaires self-assessment scores as well as their ratings during the experiments is presented. Moreover, eye and gaze features were extracted from the low level eye recorded metrics and their correlations with the participants' ratings are investigated. Finally, analysis and results are presented for machine learning approaches, for the classification of various arousal and valence levels based solely on eye and gaze features. The dataset is made publicly available and we encourage other researchers to use it for testing new methods and analytic pipelines for the estimation of an individual's affective state.TO USE THIS DATASET PLEASE CITE:Skaramagkas, V.; Ktistakis, E.; Manousos, D.; Kazantzaki, E.; Tachos, N.S.; Tripoliti, E.; Fotiadis, D.I.; Tsiknakis, M. eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset. Brain Sci. 2023, 13, 589. https://doi.org/10.3390/brainsci13040589

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 826429 (Project: SeeFar). This paper reflects only the author's view and the Commission is not responsible for any use that may be made of the information it contains. Please cite: Skaramagkas, V.; Ktistakis, E.; Manousos, D.; Kazantzaki, E.; Tachos, N.S.; Tripoliti, E.; Fotiadis, D.I.; Tsiknakis, M. eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset. Brain Sci. 2023, 13, 589. https://doi.org/10.3390/brainsci13040589