Research
Neural oscillations, electrophysiology and conscious perception.
I work on EEG and M-EEG data with computational methods, investigating how oscillatory dynamics relate to cognitive processes and to conscious visual perception.
Current
Jun 2026 — present
Research Collaboration
Cogitate Consortium · Neuronal Oscillations Group, University of Oxford
Remote
I collaborate with Prof. Ole Jensen and Dr. Xuan Cui on M-EEG analyses of prestimulus alpha-phase dynamics and conscious visual perception, using Cogitate Consortium data.
The question is how the phase of ongoing alpha activity before a stimulus relates to whether that stimulus is consciously perceived — an electrophysiological approach to the timing of perception.
Previous
Feb — Jul 2025
Research Internship — EEG & AI
Cognitive Electrophysiology Lab, University of Milan-Bicocca
Milan, Italy
- Developed and implemented machine learning pipelines for EEG-based trait classification within the Empathy and Mirror-Neurons Project.
- Performed EEG preprocessing, feature extraction and time–frequency analyses (Fourier and wavelet transforms).
- Analyzed neural oscillatory dynamics associated with action observation and empathic traits, contributing to the identification of network-level neural mechanisms underlying social cognition.
- Implemented machine learning models to classify EEG-derived neural features and identify electrophysiological biomarkers associated with cognitive traits.
- Conducted systematic literature reviews to inform experimental design and model development.
Tools · Python · ASA · EEProbe (ANT Neuro)
Research interests
Cognitive & Computational Neuroscience
Investigating the neural mechanisms underlying cognitive processes with electrophysiological and computational approaches.
EEG biomarkers and signal analysis
Preprocessing, feature extraction and classification pipelines that turn raw electrophysiology into defensible measures.
Neural dynamics and oscillations
Time–frequency structure, oscillatory phase and entrainment as a window onto how the brain organises processing in time.
Artificial Intelligence
Supervised and unsupervised machine learning and neural networks applied to small, noisy, deeply structured neural data.
Methods & tools
- Neuroscience
- EEG acquisition and analysisMNE-PythonTime–frequency analysisNeuroimaging
- Computational & statistical
- Machine learning (supervised & unsupervised)Artificial neural networksStatistical modelingFeature engineering
- Programming
- Python (NumPy, SciPy, scikit-learn)RC++SQL
- Tools
- JupyterASAEEProbeOverleaf