Kasun Amarasinghe

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About

Kasun Amarasinghe is a researcher at the forefront of brain-computer interfaces (BCI) and machine learning, with a focus on translating neural signals into practical, real-world control systems. His work centers on electroencephalography (EEG)-based BCI, particularly for robotic and prosthetic control, where he addresses the critical challenge of feature selection to improve system accuracy and responsiveness. His most-cited paper, “EEG feature selection for thought driven robots using evolutionary Algorithms” (2016, 16 citations), pioneers the use of evolutionary algorithms to optimize EEG signal processing, moving beyond traditional event-bound methods like motor imagery. This contribution is significant because it empowers users with more intuitive, continuous control over machines, expanding BCI applications beyond medical settings into everyday assistive technologies. By tackling the bottleneck of feature selection, Amarasinghe’s work enhances the reliability and speed of thought-driven systems, laying groundwork for more adaptive and user-friendly neural interfaces. His research exemplifies a blend of computational intelligence and neuroscience, offering promising pathways for next-generation human-machine interaction.