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About

Je-Hun Yu is a researcher at the forefront of human-robot interaction (HRI) and brain-computer interfaces (BCI), with a focus on integrating signal processing and machine learning to enable intuitive control of robotic systems. His work spans facial expression analysis, steady-state visual evoked potentials (SSVEP), and electromyogram (EMG)-based wearable devices. Yu’s most cited paper (8 citations) introduces a convolutional neural network and cascade detector for facial point classification, advancing emotion recognition in HRI. He has also developed BCI systems using SSVEP and EEG signals to wirelessly control robots and artificial hands, achieving classification accuracies around 70% with algorithms like LDA and SVM. Notably, Yu proposed an EMG-based wearable system for fire-detecting autonomous robots, demonstrating real-time control via Myo armband and Bluetooth communication. His research, published between 2015 and 2016, has laid groundwork for practical BCI and HRI applications, combining robust feature extraction (e.g., CPSD, common spatial patterns) with classification techniques to enhance precision and autonomy in assistive and mobile robotics.