Modeling Brain Waveforms with Convolutional Dictionary Learning and Point Processes
Speaker: Thomas Moreau (Research Scientist, INRIA Saclay)
Title: Modeling Brain Waveforms with Convolutional Dictionary Learning and Point Processes
Abstract: The quantitative analysis of non-invasive electrophysiology signals from electroencephalography (EEG) and magnetoencephalography (MEG) boils down to the identification of temporal patterns such as evoked responses, transient bursts of neural oscillations but also blinks or heartbeats for data cleaning. An emerging community aims at extracting these patterns efficiently in a non-supervised way, e.g., using Convolutional Dictionary Learning (CDL). Using low-rank atoms, multivariate CDL is able to learn not only prototypical temporal waveforms but also associated spatial patterns so their origin can be localized in the brain, which leads to an event-based description of the data. Given these events and patterns, a natural question is to estimate how their occurrences are modulated by certain cognitive tasks and experimental manipulations. To address it, we consider a Point-Process (PP) approach. While PPs have been used in neuroscience in the past, in particular for single-cell recordings (spike trains), techniques such as CDL makes them amenable to human studies based on EEG/MEG signals. We develop a novel statistical PP model – coined driven temporal point processes (DriPP) – where the intensity function of the PP model is linked to a deterministic point process corresponding to stimulation events. Results on MEG datasets demonstrate that our methodology allows revealing event-related neural responses – both evoked or induced – and isolates non-task-specific temporal patterns.
Personal website: https://tommoral.github.io/about.html
Talk in room M7 101 (Campus Monod, ENS de Lyon)