Intan Helina Hasan

Papers

Total Citations

H-Index

About

Intan Helina Hasan is a researcher focused on advancing Brain-Computer Interface (BCI) technology, with a particular emphasis on non-invasive Electroencephalogram (EEG) signal processing and its practical applications in assistive systems. Her work centers on optimizing the interface between human neural activity and external devices, addressing key challenges in signal interpretation and channel selection. Hasan’s most cited paper, “Utilization of Genetic Algorithm for Optimal EEG Channel Selection in Brain-Computer Interface Application” (2014), introduces a novel approach to reducing computational complexity by intelligently selecting the most informative EEG channels, a critical step for real-time BCI performance. This work has garnered 6 citations, reflecting its relevance in the field. She further explores direct neural control in “P300-Based EEG Signal Interpretation System for Robot Navigation Control” (2013), demonstrating how P300 event-related potentials can be harnessed for intuitive robot guidance. Through these contributions, Hasan is helping to make BCI systems more efficient and accessible, with potential impacts on assistive technology for individuals with motor disabilities. Her research bridges signal processing and practical robotics, offering a pathway toward more responsive and user-friendly neural interfaces.