Sepideh Zolfaghari

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About

Sepideh Zolfaghari is a researcher advancing the field of Brain-Computer Interfaces (BCIs), with a focused expertise in decoding motor intent from electroencephalography (EEG) signals. Her primary research areas include motor imagery classification, movement speed analysis, and the application of deep learning architectures for neural signal processing. Zolfaghari’s major contribution lies in enhancing the precision and functionality of BCI systems for rehabilitation and robotic control. Her most cited work, "Speed Classification of Upper Limb Movements Through EEG Signal for BCI Application" (2021, 10 citations), tackles the underexplored challenge of quantifying movement speed from brain signals—a critical step toward more intuitive and responsive prosthetic devices. She further demonstrated the power of convolutional neural networks (CNNs) in "Using Convolution Neural Networks Pattern for Classification of Motor Imagery in BCI System" (2020, 4 citations), showing how deep learning can reliably distinguish imagined movements. By bridging the gap between raw neural data and actionable control commands, Zolfaghari’s research directly supports the development of smarter, patient-adaptive BCIs. Her work is particularly impactful for students and engineers seeking to integrate AI with neurorehabilitation, offering a clear pathway from signal classification to real-world assistive technology.