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About

Mahnaz Arvaneh is a leading researcher at the intersection of brain-computer interfaces (BCIs), neuroprosthetics, and computational neuroscience. Her work focuses on decoding neural signals to enhance human-machine interaction, particularly for assistive technologies and clinical applications. Arvaneh’s major contributions include pioneering single-trial EEG classification of error-related potentials (ErrPs) during navigation tasks, demonstrating that the brain’s response to correct versus incorrect movements can be used as real-time feedback for semi-autonomous robot control. Her 2020 study on this topic, with 17 citations, laid groundwork for learning-based BCIs that enable robots to find quasi-optimal routes. She further advanced error detection by developing relative peak features to improve EEG-based ErrP classification, addressing a key challenge in BCI reliability. In 2021, Arvaneh extended her work to neuropsychiatric disorders, designing a multimodal neuroprosthetic interface to record, modulate, and classify electrophysiological biomarkers relevant to conditions like addiction and schizophrenia. Her research has been cited over 40 times, reflecting its impact on both BCI engineering and clinical neuroscience. Arvaneh’s innovative four-way classification of EEG responses to virtual robot navigation showcases her ability to translate complex neural dynamics into practical control systems, making her a pivotal figure in next-generation neurotechnology.