eSEEd: emotional State Estimation based on Eye-tracking dataset

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

本研究提出eSEEd数据集——基于眼动追踪（eye-tracking）的情绪状态估计数据库。
招募48名受试者，采集其观看情绪诱发视频时的眼动数据：每名受试者先后观看10段情绪诱发视频，每段视频结束后均衔接一段中性视频。
受试者需对5种情绪——柔情（tenderness）、愤怒（anger）、厌恶（disgust）、悲伤（sadness）、中性（neutral）——进行0至10分的评分，后续将评分转换为情绪唤醒度（arousal）与效价（valence）水平。
此外，每名受试者需填写3份自我评估问卷。
本研究对受试者的问卷作答结果、自我评估得分以及实验过程中的情绪评分展开了全面分析。
此外，从原始眼动记录指标中提取眼动与注视特征，并探究其与受试者情绪评分的相关性。
最后，本研究针对仅基于眼动与注视特征的情绪唤醒度、效价水平分类任务，展示了对应的机器学习方法分析及实验结果。
本数据集已公开，我们鼓励其他研究人员使用该数据集，以测试用于个体情感状态估计的新型方法与分析流程。
使用本数据集请引用：
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