Alexandre Aksenov
Papers
1
Total Citations
2
H-Index
1
About
Alexandre Aksenov is a researcher at the forefront of motor Brain–Computer Interfaces (BCIs), specializing in advanced signal processing and machine learning for real-time neural decoding. His work focuses on developing adaptive algorithms that enable precise control of complex effectors, such as robotic arms and exoskeletons, for patients with severe motor impairments. Aksenov’s key contribution lies in the creation of the "Online adaptive group-wise sparse Penalized Recursive Exponentially Weighted N-way Partial Least Square" method, a novel approach that enhances the accuracy and robustness of intracranial BCI decoding. This work, published in 2023, has already garnered 2 citations, reflecting its emerging impact in the field. By addressing the challenges of high-resolution neural data and real-time adaptation, Aksenov’s research bridges the gap between theoretical machine learning and practical clinical applications. His achievements highlight a commitment to restoring communication and mobility for individuals with paralysis, positioning him as a rising innovator in neural engineering.
Research Focus
Key Achievements
Top Papers
- 1