Tiago Becker
Papers
2
Total Citations
9
H-Index
2
About
Tiago Becker’s research bridges biomedical engineering and robotics, with a focus on signal processing and mechanical systems. His most cited work, “Round Cosine Transform Based Feature Extraction of Motor Imagery EEG Signals” (2018, 6 citations), introduces a novel method for decoding brain-computer interface signals, enhancing the classification of motor imagery tasks—a key step toward non-invasive neural control. This contribution holds promise for assistive technologies and neurorehabilitation. In parallel, his study “Analytical and Experimental Analysis of Friction Forces inside Curved Pipes” (2017, 3 citations) addresses a critical challenge in deep-sea robotics: calculating the forces needed to pull umbilical cables through curved, oil-filled pipes for in-pipe robots used in hydrate plug removal. By combining analytical models with experimental validation, Becker provides a practical framework for improving the reliability of subsea operations. Though early in his career, his work demonstrates versatility—from EEG-based feature extraction to friction mechanics—and lays groundwork for future advances in both neural interfaces and offshore robotics.
Research Focus
Key Achievements
Top Papers
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