Hugo Weissbart

Hugo Weissbart is a Research Fellow at the Donders Institute for Brain, Cognition and Behaviour, Radboud University, specializing in neural mechanisms of language processing and brain-computer interfacing. His work bridges cognitive neuroscience, neurotechnology, and computational linguistics.

His educational background includes:

• PhD in Neurotechnology (2020) from Imperial College London
• MRes in Neurotechnology (2016) from Imperial College London
• MSc in Physics (2013) from Imperial College London
• MSc in Aerospace Engineering (2014) from ISAE-SUPAERO

Research focuses on predictive language processing mechanisms across phoneme, lexical, and syntactic levels using MEG to study how slow linguistic dynamics modulate high-frequency neural activity. Current projects involve developing Brain-Computer Interfaces for communication through neural decoding of speech comprehension and auditory attention. His methodology emphasizes naturalistic stimuli analysis and cross-frequency dynamics in spoken language comprehension.

Software contributions include the pyEEG library for M/EEG analysis with naturalistic stimuli and feature-PAC code for cross-frequency dynamics research. He actively develops open-source tools and created educational materials like the CNSP 2025 Workshop tutorial on non-linear temporal response function analysis.

Professional activity shows consistent GitHub contributions with 184 commits in the last year, primarily in Python and Jupyter Notebook repositories related to neural decoding and EEG analysis. His ORCID (0000-0003-2820-3865) links to scholarly works in cognitive neuroscience and neuroengineering.