Mingming Chen

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About

Mingming Chen is a researcher at the forefront of brain-computer interfaces (BCI) and neurorehabilitation, with a primary focus on decoding motor imagery (MI) from electroencephalography (EEG) signals. Her most impactful contribution is the development of a multi-branch fusion convolutional neural network (CNN) that enables the recognition of single upper limb motor imagery tasks—a significant advance over conventional bilateral limb paradigms. This work, published in 2023 and garnering 24 citations, holds transformative potential for neuroprosthetics and robot control, offering more natural and precise control for individuals with motor impairments. Chen’s research addresses a critical gap in MI-BCI by expanding the repertoire of detectable movements, thereby enhancing the practicality of non-invasive brain-controlled devices. While her earlier work on text mining in cloud computing (2017) has been retracted, her recent EEG-focused studies demonstrate a clear pivot toward high-impact neural engineering. With a growing citation footprint, Chen is establishing herself as a key contributor to the next generation of adaptive, user-centric neurorehabilitation technologies.