Questions, methods & open code
I study how brain activity becomes perception, decisions, and behavior—and how that activity differs across people and contexts.
These questions share a toolbox. I compare brains with neural networks, use foundation models on clinical EEG, and build software that carries methods from one project to another.
Select a question or a shared method. Follow the overlaps.
Neural dynamics, learned representations, and prediction connect these research directions. Each question below shows the methods it shares with the others.
A shared toolbox
My research moves between understanding brains and building models. The overlaps are where those approaches meet.
For example, clinical EEG brings together brain dynamics, learned representations, and prediction.
Select a question to explore its work.
Select a method to follow its connections.
Shared method
Rhythms, complexity, and trajectories connect decisions with differences across people, clinical contexts, and brain states.
Connected research
Shared method
Model representations connect human perception, EEG/MEG foundation models, and clinical-data analysis.
Connected research
Shared method
Decoding, behavioral models, and careful evaluation help distinguish useful information from patterns that do not generalize.
Connected research
Research question
How I approach it
MEG and EEG · Behavioral models · Neural trajectories · Oscillatory coupling · Time-resolved decoding
Explore Decisions & commitmentTools in this work
Research question
How I approach it
EEG and MEG · Source reconstruction · Task-optimized neural networks · Representational similarity analysis
Explore Perception in brains & models 4 related papersResearch question
How I approach it
Resting-state MEG · High-density EEG · Spectral analysis · Aperiodic activity · Complexity and entropy · Machine learning
Explore Brain states & cognition 2 related papersResearch question
How I approach it
Clinical EEG · Spectral and complexity measures · Foundation-model representations · Dimensionality reduction · Machine learning
Explore Clinical questions & EEG 1 related paperTools in this work
Research question
How I approach it
Representation learning · Foundation models · Cross-dataset evaluation · Brain decoding · Reproducible analysis
Explore Brain-signal models & methods 7 related papersTools in this work
When the evidence changes, how do we revise a choice—and when do we stop?
My PhD asks how urgency, communication between brain regions, and ongoing brain dynamics shape the transition from weighing evidence to committing to an action.
What does a model need to learn to capture how humans perceive faces and expressions?
I compare human EEG/MEG responses with artificial neural-network representations to study face familiarity and emotion perception, and how training shapes brain–model correspondence.
How do ongoing brain dynamics relate to differences in cognition and experience?
Collaborative work on resting-state rhythms, verbal learning, and non-ordinary states of consciousness asks how brain activity varies across people and states.
Which features of brain signals help us understand clinical differences—and which carry across people?
At CHU Sainte-Justine, I combine EEG descriptors and foundation-model representations to study pediatric clinical questions. A broader clinical collaboration uses machine learning to study cognitive decline.
What do our models learn from brain signals, and when can we trust what transfers?
I develop and evaluate EEG/MEG representations, contribute to decoding benchmarks, and build open methods. These tools run through my work on perception, decisions, and clinical data.
From analysis to shared tools
The questions lead me to build tools for cleaning signals, comparing representations, and making analyses reusable. The project pages link to their analysis repositories and to the packages that support them.
Explore the software and my contributions