Tetiana Aksenova

Université Joseph Fourier, Commissariat à l'Énergie Atomique et aux Énergies Alternatives

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About

Tetiana Aksenova’s research lies at the dynamic intersection of neural engineering and computational neuroscience, with a primary focus on brain–computer interfaces (BCIs) and deep brain stimulation (DBS). Her most cited work, “Deep brain stimulation” (2011, 21 citations), has contributed foundational insights into neuromodulation techniques for treating movement disorders. More recently, she has pioneered advanced signal processing methods for real-time neural decoding, exemplified by her 2023 paper on online adaptive group-wise sparse penalized recursive exponentially weighted N-way partial least squares for epidural intracranial BCIs. This work addresses the critical challenge of controlling complex effectors—such as robotic arms and exoskeletons—by decoding high-resolution neural signals from patients with severe motor impairments. Her contributions are particularly impactful in the field of motor BCIs, where she develops algorithms that adapt to changing neural patterns, improving the reliability and responsiveness of prosthetic devices. With a career spanning over a decade, Aksenova’s research continues to push the boundaries of how we translate brain activity into actionable commands, offering new hope for restoring mobility and independence to individuals with paralysis.