Nafas Saffaryazdi

Nafas Saffaryazdi

Virtual Intern

Nafas joined the ECL lab as a virtual intern in 2021. She is working on Recognizing emotion in the conversational audio project. 

She got her master’s degree in Artificial Intelligence at Azad University، Science And Research Branch of Tehran.

She has worked as a software developer in some IT companies, and now she is a software developer at Behsazan Mellat company in Iran.

She is interested in sentiment analysis in texts. She has worked on some projects about Data Science and text mining in social media for Persian texts in APA Security and Cyber Research Center of Tehran Azad University in Iran.


  • Emotion Recognition in Conversations Using Brain and Physiological Signals
    Nastaran Saffaryazdi , Yenushka Goonesekera , Nafiseh Saffaryazdi , Nebiyou Daniel Hailemariam , Ebasa Girma Temesgen , Suranga Nanayakkara , Elizabeth Broadbent , Mark Billinghurst

    Saffaryazdi, N., Goonesekera, Y., Saffaryazdi, N., Hailemariam, N. D., Temesgen, E. G., Nanayakkara, S., ... & Billinghurst, M. (2022, March). Emotion Recognition in Conversations Using Brain and Physiological Signals. In 27th International Conference on Intelligent User Interfaces (pp. 229-242).

    title={Emotion recognition in conversations using brain and physiological signals},
    author={Saffaryazdi, Nastaran and Goonesekera, Yenushka and Saffaryazdi, Nafiseh and Hailemariam, Nebiyou Daniel and Temesgen, Ebasa Girma and Nanayakkara, Suranga and Broadbent, Elizabeth and Billinghurst, Mark},
    booktitle={27th International Conference on Intelligent User Interfaces},
    Emotions are complicated psycho-physiological processes that are related to numerous external and internal changes in the body. They play an essential role in human-human interaction and can be important for human-machine interfaces. Automatically recognizing emotions in conversation could be applied in many application domains like health-care, education, social interactions, entertainment, and more. Facial expressions, speech, and body gestures are primary cues that have been widely used for recognizing emotions in conversation. However, these cues can be ineffective as they cannot reveal underlying emotions when people involuntarily or deliberately conceal their emotions. Researchers have shown that analyzing brain activity and physiological signals can lead to more reliable emotion recognition since they generally cannot be controlled. However, these body responses in emotional situations have been rarely explored in interactive tasks like conversations. This paper explores and discusses the performance and challenges of using brain activity and other physiological signals in recognizing emotions in a face-to-face conversation. We present an experimental setup for stimulating spontaneous emotions using a face-to-face conversation and creating a dataset of the brain and physiological activity. We then describe our analysis strategies for recognizing emotions using Electroencephalography (EEG), Photoplethysmography (PPG), and Galvanic Skin Response (GSR) signals in subject-dependent and subject-independent approaches. Finally, we describe new directions for future research in conversational emotion recognition and the limitations and challenges of our approach.