Anam Hashmi

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About

Anam Hashmi is a researcher at the forefront of brain-computer interface (BCI) technology, specializing in the intersection of machine learning and neurorehabilitation. Her work focuses on decoding motor imagery signals from electroencephalography (EEG) to power assistive technologies. In her most cited work, "A Comparative Study of Machine Learning Algorithms for EEG Signal Classification" (2021, 5 citations), Hashmi systematically evaluated algorithms including SVM, Random Forest, and Autoencoder hybrids to identify robust classification methods for neural signals. She extended this research in "EEG-Based Exoskeleton for Rehabilitation Therapy" (2021, 4 citations), directly applying these techniques to develop brain-controlled exoskeletons for patients with motor impairments. Her study on classifying imagined hand movements using Random Forest algorithms (2021, 2 citations) further refined methods for distinguishing between relaxation and motor intention states. By rigorously comparing machine learning tools and demonstrating their real-world application in rehabilitation devices, Hashmi is helping bridge the gap between raw neural data and practical therapeutic solutions—work that holds significant promise for restoring mobility to individuals with paralysis or stroke-related disabilities.