Research collaboration — Cogitate Consortium · University of Oxford
PasqualeScognamiglio
Cognitive & Computational Neuroscience Researcher
I study how neural oscillations shape cognition and conscious perception, using EEG electrophysiology and machine learning.
Profile
Cognitive and computational neuroscience researcher with an MSc in Human-Centered Artificial Intelligence, specializing in EEG electrophysiology, neural oscillations and machine learning.
Co-author of peer-reviewed publications investigating neural dynamics using time–frequency analysis, and experienced in developing computational pipelines for neural signal processing and classification.
My aim is to pursue a PhD in cognitive neuroscience, applying electrophysiological and computational approaches to the neural mechanisms underlying cognitive processes.
Current research
Prestimulus alpha-phase dynamics and conscious visual perception.
Cogitate Consortium · Neuronal Oscillations Group, University of Oxford
Jun 2026 — present · 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.
Research in detail →Publications
- 2026
Embodied neural synchrony to rhythmic structure: An ERP and frequency-domain investigation of beat entrainment
A. M. Proverbio, P. Scognamiglio, M. Valtolina, A. Zani
International Journal of Psychophysiology, 220, 113303
DOI → - 2025
Empathic Traits Modulate Oscillatory Dynamics Revealed by Time-Frequency Analysis During Body Language Reading
A. M. Proverbio, P. Scognamiglio
Brain Sciences, 15(7), 673
DOI →
Research focus
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
Background
2020 — 2023
B.Sc. Computer Engineering
University of Naples Federico II, Naples, Italy
110/110 cum laude
2023 — 2025
M.Sc. Human-Centered Artificial Intelligence (Neuro AI)
University of Milan · University of Milan-Bicocca · University of Pavia, Italy
2025
M.Sc. Neuro-X
EPFL, Lausanne, Switzerland
Selected projects
- 2025
EEG–MEG Comparative Study for Visual Neural Decoding
Investigated EEG, MEG and multimodal M/EEG neural decoding in a visual perception task, comparing feature-based machine learning and end-to-end deep learning models to analyze modality complementarity, temporal dynamics and data efficiency.
- 2025
Personalized Neuromodulation Treatment for Apathy
Designed a personalized neuromodulation protocol for apathy based on tTIS, integrating computational modeling of effort–reward decision-making, behavioral phenotyping, EEG biomarkers and MRI-informed stimulation of cortico-striatal networks.
- 2025
MedAIx — Clinical Decision-Making Prototype
HCI system testing how AI communication style impacts trust in clinical decision-making.
- 2025
Early Diagnosis of Alzheimer's Disease
Machine learning pipeline on the OASIS-1 dataset, achieving 90.8% accuracy with gradient boosting.
Beyond research
Music · Photography · Travel · Sport