Miao Shi

Papers

Total Citations

H-Index

About

Miao Shi is a researcher whose work sits at the intersection of biomedical signal processing and computational intelligence, with a particular focus on electroencephalography (EEG) analysis and feature selection. Their most notable contribution, the "Multi-objective squirrel search algorithm for EEG feature selection" (2023), introduces a novel metaheuristic optimization method that balances classification accuracy and feature reduction in high-dimensional EEG datasets. This work, which has already garnered 2 citations, demonstrates Shi’s ability to adapt nature-inspired algorithms for real-world biomedical challenges, offering a more efficient approach to identifying critical neural markers. Beyond this flagship paper, Shi’s research portfolio spans machine learning applications in healthcare, where they have explored how swarm intelligence and multi-objective optimization can streamline diagnostic processes. Their work is particularly relevant for researchers developing brain-computer interfaces or automated seizure detection systems, as it addresses the perennial challenge of extracting meaningful patterns from noisy, high-dimensional neural data. By bridging algorithmic innovation with practical clinical needs, Miao Shi is contributing to a future where intelligent systems can more reliably interpret the brain’s electrical signals.