Eduardo Lopez-Arce Vivas
Tecnológico de Monterrey, Centro de Investigación y Proyectos en Ambiente y Desarrollo
Papers
2
Total Citations
19
H-Index
2
About
Eduardo Lopez-Arce Vivas is a researcher specializing in brain-machine interfaces (BMI) and biomedical signal processing, with a focus on translating neural and physiological signals into practical control commands. His work centers on developing algorithms that decode electroencephalographic (EEG) and electrooculographic (EOG) signals to enable intuitive human-machine interaction. In his most cited paper, "Discrete Wavelet transform and ANFIS classifier for Brain-Machine Interface based on EEG" (2013, 16 citations), he introduced an online BMI system that uses EEG bipolar connections to control a robotic hand through eye closure—a notable step toward accessible, real-time assistive technology. His earlier work, "Algorithm to detect six basic commands by the analysis of electroencephalographic and electrooculographic signals" (2012, 3 citations), explored multimodal signal fusion to enhance command robustness. Though his citation counts are modest, Lopez-Arce Vivas’ contributions are significant for their practical, low-cost approach to BMI design, emphasizing real-world usability over complexity. His research bridges signal processing, machine learning, and neuroengineering, offering a foundation for future developments in non-invasive neural control systems.
Research Focus
Key Achievements
Top Papers
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