Improving state-of-the-art emotion detection with human signals / Spring 2019 - Master Thesis / Semester Project
This project brings together two topics from our previous work: On one hand, we have a built a state-of-the-art model for emotion detection in context . On the other hand, we have a dataset of recorded eye-tracking and EEG signals for subjects reading text . Preliminary experiments  and previous research  show that these human signals are valuable for emotion detection.
The goal of this thesis or semester project is to improve the performance of the emotion detection system by developing a method of extracting or predicting brain activity signals relevant for this task.
Supervised by Nora Hollenstein
-  SemEval 2019 - EmoContext
-  Hollenstein N., Rotsztejn J., Troendle M., Pedroni A., Zhang C. and Langer N. “ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading.” Scientific Data. 2018.
-  Rotsztejn, J. (2018). Learning from Cognitive Features to Support Natural Language Processing Tasks (Master thesis, ETH Zurich).
-  Li, M., & Lu, B. L. (2009, September). Emotion classification based on gamma-band EEG. In Engineering in medicine and biology society, 2009. EMBC 2009. Annual international conference of the IEEE (pp. 1223-1226). IEEE.