Andreas Schwarz

Papers

Total Citations

322

H-Index

About

Andreas Schwarz is a neuroscience and biomedical engineering researcher whose work sits at the intersection of brain-computer interfaces (BCIs), neural signal processing, and assistive technology. His research focuses on decoding motor intentions from non-invasive electroencephalography (EEG) signals, with the overarching goal of restoring independence to individuals with motor impairments such as spinal cord injury. Schwarz's most influential contribution, "Upper limb movements can be decoded from the time-domain of low-frequency EEG" (2017), has garnered over 250 citations and demonstrated that complex upper limb movements could be reliably distinguished using low-frequency EEG signals — a finding with significant implications for BCI design. Building on this foundation, his 2020 work on decoding hand movements to control a robotic arm in simulation environments (62 citations) translated these neural decoding principles into practical assistive applications. His research also addresses real-world clinical populations, exploring non-invasive BCI systems specifically tailored for users with spinal cord injury. Collectively, Schwarz's work advances our understanding of how movement is neurally encoded and pushes the boundaries of what non-invasive BCIs can achieve for people with severe physical disabilities.