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

• URL: http://arxiv.org/abs/2112.06652v1
• Date: Wed, 8 Dec 2021 13:07:21 GMT
• Title: DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals
• Authors: C\'edric Allain (PARIETAL), Alexandre Gramfort (PARIETAL), Thomas Moreau (PARIETAL), A Preprint
• Abstract summary: We develop a novel statistical point process model-called driven temporal point processes (DriPP) We derive a fast and principled expectation-maximization (EM) algorithm to estimate the parameters of this model. Results on standard MEG datasets demonstrate that our methodology reveals event-related neural responses.
• Score: 62.997667081978825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. 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.

Related papers

• Neural decoding from stereotactic EEG: accounting for electrode variability across subjects [21.28778005847666]
  We introduce seegnificant: a training framework that can be used to decode behavior across subjects using sEEG data. We construct a multi-subject model trained on the combined data from 21 subjects performing a behavioral task.
  arXiv  Detail & Related papers  (2024-11-01T17:58:01Z)
• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates [54.96885726053036]
  This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks.
  arXiv  Detail & Related papers  (2024-06-03T16:30:19Z)
• Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification [50.63919418371698]
  Tactile signals collected by wearable electronics are essential in modeling and understanding human behavior. Existing action classification methods fail to capture the spatial and temporal features of tactile signals simultaneously. We propose S-Temporal Aware Aware Transformer (STAT) to utilize continuous tactile signals for action classification.
  arXiv  Detail & Related papers  (2024-01-21T03:47:57Z)
• Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes [29.592870472342337]
  This work presents the temporal conditioning spiking latent variable models (TeCoS-LVM) to simulate the neural response to natural visual stimuli. We use spiking neurons to produce spike outputs that directly match the recorded trains. We show that TeCoS-LVM models can produce more realistic spike activities and accurately fit spike statistics than powerful alternatives.
  arXiv  Detail & Related papers  (2023-06-21T06:30:18Z)
• Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning [9.234802409391111]
  We propose a novel method based on deep learning for timing analysis of modularized detectors. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model.
  arXiv  Detail & Related papers  (2023-04-24T09:16:31Z)
• Decision SincNet: Neurocognitive models of decision making that predict cognitive processes from neural signals [0.0]
  We adapted a SincNet-based shallow neural network architecture to fit the Drift-Diffusion model using EEG signals. Single-trial estimates of drift and boundary performed better at predicting RTs than the median estimates in both training and test data sets.
  arXiv  Detail & Related papers  (2022-08-04T18:51:29Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Learning Accurate Business Process Simulation Models from Event Logs via Automated Process Discovery and Deep Learning [0.8164433158925593]
  Data-Driven Simulation (DDS) methods learn process simulation models from event logs. Deep Learning (DL) models are able to accurately capture such temporal dynamics. This paper presents a hybrid approach to learn process simulation models from event logs.
  arXiv  Detail & Related papers  (2021-03-22T15:34:57Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.