Shan Fu

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About

Shan Fu is a researcher at the forefront of affective computing and human-robot interaction, with a particular focus on decoding human emotional states through electroencephalography (EEG) signals. Their most-cited work, "Affective Recognition Using EEG Signal in Human-Robot Interaction" (2018), has garnered 7 citations and represents a foundational contribution to the field. In this study, Fu developed a framework for real-time emotion recognition by analyzing brainwave patterns, enabling robots to respond adaptively to human affective cues. This work bridges cognitive neuroscience and robotics, offering a pathway toward more empathetic and intuitive human-machine interfaces. By demonstrating that EEG-based affective recognition can be effectively integrated into interactive systems, Fu has opened new avenues for assistive technologies, mental health monitoring, and socially aware robotics. Their research underscores the potential of non-invasive neural signals to transform how machines perceive and react to human emotions, making interactions more natural and responsive. Shan Fu’s contributions are particularly valuable for students and researchers exploring the intersection of biosignal processing, artificial intelligence, and human-centered design.