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About

Elnaz Lashgari is a researcher at the intersection of neuroscience, machine learning, and human-machine interaction, with a primary focus on decoding neural and muscular signals for assistive and rehabilitative technologies. Her work centers on two key areas: motor-imagery classification from electroencephalography (EEG) and electromyography (EMG) pattern recognition for grasping and locomotion. Her most impactful contribution is an end-to-end convolutional neural network with an attentional mechanism applied to raw EEG for brain-computer interface (BCI) tasks—a paper that has garnered 48 citations and represents a significant advance in real-time, user-friendly BCI systems. Lashgari has also pioneered the use of dimensionality reduction and manifold learning techniques, such as Laplacian eigenmaps, to classify object weight from EMG during human grasping and to decode muscle activation patterns in running. Her work on EMG-based classification during reach-to-grasp motion further extends the potential for intuitive prosthetic control. With a growing citation record and a focus on translating noisy, high-dimensional biological signals into actionable commands, Lashgari’s research is paving the way for more responsive assistive robots, neurorehabilitation tools, and seamless human-machine interfaces.