Yeou‐Jiunn Chen

Southern Taiwan University of Science and Technology

Papers

Total Citations

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About

Dr. Yeou-Jiunn Chen is a leading researcher in brain-computer interfaces (BCI), with a focus on steady-state visual evoked potentials (SSVEP) for assistive technology. His work centers on developing practical, single-channel SSVEP-based systems that integrate fuzzy logic algorithms to enhance real-time decision-making and user control. Chen’s major contributions include pioneering BCI applications for mobility and daily assistance, such as an automatic feeding robot and a maze game designed to improve quality of life for individuals with motor neuron disease. His most cited paper (2017, 39 citations) introduces a fuzzy feature threshold algorithm that significantly boosts classification accuracy in a single-channel SSVEP maze game, demonstrating how BCI can be made more accessible and efficient. Another influential study (2016, 18 citations) applies fuzzy decision-making to control a feeding robot, showcasing the translation of neural signals into practical actions. With over 70 total citations across his key works, Chen’s research bridges the gap between theoretical BCI advances and real-world assistive devices, offering hope for enhanced autonomy in patients with severe motor impairments. His innovative use of fuzzy logic in BCI systems stands as a notable achievement in the field.