Nejla Ghaboosi

Papers

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About

Nejla Ghaboosi is a leading researcher in the field of brain-computer interfaces (BCIs), with a particular focus on hybrid systems and motor imagery decoding. Her most impactful contribution is the development of **gumpy**, a free and open-source Python toolbox designed specifically for hybrid BCIs. This toolbox, detailed in her highly cited 2018 paper (44 citations), provides state-of-the-art signal processing algorithms and has become a valuable resource for the BCI community, enabling researchers to more easily implement and test complex hybrid systems. Ghaboosi has also made significant strides in validating deep neural networks for online decoding of motor imagery movements from EEG signals, as demonstrated in her 2018 work (8 citations). This research addresses a critical challenge in non-invasive BCIs—translating a user's motor intention into reliable control signals—by rigorously testing the real-world applicability of deep learning models. Through her work, Ghaboosi is helping to bridge the gap between advanced computational methods and practical, real-time BCI applications, making her a key contributor to the advancement of accessible and robust neural interfaces.