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About

Olga Sourina is a researcher whose work sits at the fascinating intersection of human-computer interaction, neuroscience, and robotics design. Her research primarily focuses on leveraging biometric signals — particularly electroencephalogram (EEG) data and eye-tracking — to decode human perception, emotion, and cognitive states in ways that can meaningfully inform technology design. Sourina's most impactful contribution comes from her pioneering investigations into how humans perceive humanoid robot aesthetics. Her 2019 study on detecting robot design preferences using EEG and eye-tracking (19 citations) demonstrated that neurophysiological signals could objectively capture aesthetic responses that traditional surveys might miss, offering actionable guidance to robot designers. This thread of inquiry continued through subsequent work evaluating global eye-tracking metrics for robot appearance assessment and, most recently, autoencoder-driven analytics for deeper preference modeling. Beyond robotics, her 2013 work on fractal-based brain state recognition from EEG (12 citations) reflects her broader commitment to advancing brain-computer interface methodologies, while her early exploration of emotion-based interaction established foundational interest in affective computing. Across her career, Sourina has championed the idea that human neurological responses are powerful, underutilized tools for shaping more intuitive and appealing technologies.