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About

Faiza Renaldi is a researcher at the forefront of Brain-Computer Interface (BCI) technology, specializing in translating neural signals into real-world control systems. Her work focuses on developing non-invasive methods to interpret electroencephalography (EEG) data, enabling direct communication between the human brain and external devices. Renaldi’s key contributions include pioneering the use of Fast Fourier Transform and Learning Vector Quantization to create an arcade game controlled entirely by thought, a project that garnered 29 citations and demonstrated the practical potential of BCI for entertainment. She further advanced the field by integrating wavelet functions with Recurrent Neural Networks to classify emotional states, allowing a robot simulator to be controlled by a user’s emotions within seconds—a study cited 13 times. This work bridges affective computing and robotics, offering new pathways for assistive technologies. With a cumulative impact of over 45 citations, Renaldi’s research is foundational for students and engineers exploring real-time EEG processing, pattern recognition, and human-machine interaction. Her notable achievement lies in making BCI accessible and engaging, proving that complex neural data can power intuitive, responsive systems.