English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The quantitative analysis of non-invasive electrophysiology signals fromelectroencephalography (EEG) and magnetoencephalography (MEG) boils down to theidentification of temporal patterns such as evoked responses, transient burstsof neural oscillations but also blinks or heartbeats for data cleaning. Severalworks have shown that these patterns can be extracted efficiently in anunsupervised way, e.g., using Convolutional Dictionary Learning. This leads toan event-based description of the data. Given these events, a natural questionis to estimate how their occurrences are modulated by certain cognitive tasksand experimental manipulations. To address it, we propose a point processapproach. While point processes have been used in neuroscience in the past, inparticular for single cell recordings (spike trains), techniques such asConvolutional Dictionary Learning make them amenable to human studies based onEEG/MEG signals. We develop a novel statistical point process model-calleddriven temporal point processes (DriPP)-where the intensity function of thepoint process model is linked to a set of point processes corresponding tostimulation events. We derive a fast and principled expectation-maximization(EM) algorithm to estimate the parameters of this model. Simulations revealthat model parameters can be identified from long enough signals. Results onstandard MEG datasets demonstrate that our methodology reveals event-relatedneural responses-both evoked and induced-and isolates non-task specifictemporal patterns.

DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals