Mikel Val-Calvo
Papers
8
Total Citations
193
H-Index
5
About
Mikel Val-Calvo is a researcher specializing in affective computing, human-robot interaction (HRI), and multimodal emotion recognition, with a particular focus on bridging artificial intelligence and real-time physiological signal processing. His work addresses one of the most challenging frontiers in computer vision and robotics: enabling machines to perceive, interpret, and respond to human emotional states with accuracy and efficiency. Val-Calvo has made significant contributions through the development of deep neural network ensembles for facial expression recognition, achieving robust real-time performance despite the persistent problem of mislabeled training data — a methodological challenge he directly confronted in his research. His 2020 papers on facial emotion recognition and affective story-telling HRI each garnered 46 citations, reflecting strong community interest. Beyond facial cues, he has pioneered multimodal approaches integrating EEG, galvanic skin response, and heart rate signals, with optimized lightweight pipelines suitable for wearable, real-world deployment — a landmark contribution cited 37 times. His exploration of EEG lateralization for emotion classification further demonstrates methodological ingenuity. Collectively, Val-Calvo's portfolio bridges neuroscience, robotics, and machine learning, making his work essential reading for researchers designing emotionally intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 7Exploring the Physiological Basis of Emotional HRI Using a BCI Interface3 citations · 2017
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