FatosXhafaUPC/MDEAW-A-Multimodal-Dataset-for-Emotion-Analysis: First Release of MDEAW Dataset

We present MDEAW, a multimodal database consisting of Electrodermal Activity (EDA) and Photoplethysmography (PPG) signals recorded during the exams for the course taught by the teacher at Eurecat Academy, Sabadell, Barcelona in order to elicit the emotional reactions to the students in a class room scenario. Signals from 10 students were recorded along with the students self-assessment of their affective state after each stimuli, in terms 6 basic emotion states. All the signals were captured using portable, wearable, wireless, low-cost, and offthe- shelf equipment that has the potential to allow the use of affective computing methods in everyday applications. A baseline for student-wise affect recognition using EDA and PPG-based features, as well as their fusion, was established through ReMECS, Fed-ReMECS and Fed-ReMECS-U. These results indicate the prospects of using low-cost devices for affective state recognition applications. The proposed database will be made publicly available in order to allow researchers to achieve a more thorough evaluation of the suitability of these capturing devices for emotion state recognition applications.