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About

Zied Tayeb is a leading researcher in the field of brain-computer interfaces (BCIs) and neural signal processing, with a particular focus on hybrid and real-time systems. His most cited work, "Gumpy: a Python toolbox suitable for hybrid brain–computer interfaces" (2018, 44 citations), introduced a powerful open-source framework that integrates state-of-the-art algorithms and signal processing methods, enabling researchers to develop and test hybrid BCIs that combine multiple neural signals. Tayeb has also made significant contributions to understanding the brain's response to thermal stimuli, identifying distinct spatio-temporal and spectral patterns for different thermal perceptions (2022, 13 citations), with clinical implications for conditions like phantom-limb pain. His work on validating deep neural networks for online decoding of motor imagery from EEG signals (2018, 8 citations) has advanced the practical application of non-invasive BCIs for motor control. Additionally, Tayeb developed a real-time human-robot interface using EOG and EMG signals to control reach-to-grasp movements (2020, 4 citations), demonstrating the translational potential of his research. His contributions have been recognized for bridging the gap between theoretical algorithms and practical, real-world BCI applications.