Agata Manolova

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About

Agata Manolova is a leading researcher at the intersection of biomedical signal processing and human-computer interaction, with a core focus on brain-computer interfaces (BCI) and neuromuscular fatigue assessment. Her most cited work, "Combined EEG and EMG fatigue measurement framework with application to hybrid brain-computer interface" (2016, 13 citations), introduces a pioneering dual-modality framework that simultaneously leverages electroencephalography (EEG) and electromyography (EMG) signals. This approach enables more robust and accurate detection of muscle fatigue, directly addressing a critical bottleneck in real-world BCI applications—namely, the degradation of system performance due to user fatigue. By integrating neural and muscular activity, Manolova’s framework enhances the reliability of hybrid BCIs, making them more practical for assistive technologies and rehabilitation robotics. Her contributions are particularly notable for bridging engineering, neuroscience, and clinical practice, reflecting a multidisciplinary approach that has informed subsequent work in adaptive human-machine systems. With a growing citation footprint, Manolova’s research continues to shape how we design fatigue-resilient, user-centered interfaces, positioning her as a key voice in the evolution of non-invasive neural control systems.