  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. Several
works have shown that these patterns can be extracted efficiently in an
unsupervised way, e.g., using Convolutional Dictionary Learning. This leads to
an event-based description of the data. Given these events, a natural question
is to estimate how their occurrences are modulated by certain cognitive tasks
and experimental manipulations. To address it, we propose a point process
approach. While point processes have been used in neuroscience in the past, in
particular for single cell recordings (spike trains), techniques such as
Convolutional Dictionary Learning make them amenable to human studies based on
EEG/MEG signals. We develop a novel statistical point process model-called
driven temporal point processes (DriPP)-where the intensity function of the
point process model is linked to a set of point processes corresponding to
stimulation events. We derive a fast and principled expectation-maximization
(EM) algorithm to estimate the parameters of this model. Simulations reveal
that model parameters can be identified from long enough signals. Results on
standard MEG datasets demonstrate that our methodology reveals event-related
neural responses-both evoked and induced-and isolates non-task specific
temporal patterns.
