Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models

• URL: http://arxiv.org/abs/2403.18486v1
• Date: Wed, 27 Mar 2024 11:58:45 GMT
• Title: Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models
• Authors: Guido Klein, Pierre Guetschel, Gianluigi Silvestri, Michael Tangermann,
• Abstract summary: We introduce a novel approach to conditional diffusion models that directly generate subject-, session-, and class-specific EEG data. The results indicate that the proposed model can generate EEG data that resembles real data for each subject, session, and class.
• Score: 3.187381965457262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data scarcity in the brain-computer interface field can be alleviated through the use of generative models, specifically diffusion models. While diffusion models have previously been successfully applied to electroencephalogram (EEG) data, existing models lack flexibility w.r.t.~sampling or require alternative representations of the EEG data. To overcome these limitations, we introduce a novel approach to conditional diffusion models that utilizes classifier-free guidance to directly generate subject-, session-, and class-specific EEG data. In addition to commonly used metrics, domain-specific metrics are employed to evaluate the specificity of the generated samples. The results indicate that the proposed model can generate EEG data that resembles real data for each subject, session, and class.

Related papers

• Can EEG resting state data benefit data-driven approaches for motor-imagery decoding? [4.870701423888026]
  We propose a feature concatenation approach to enhance decoding models' generalization. We combine the EEGNet model, a standard convolutional neural network for EEG signal classification, with functional connectivity measures derived from resting-state EEG data. While an improvement in mean accuracy for within-user scenarios is observed, concatenation doesn't benefit across-user scenarios when compared with random data concatenation.
  arXiv  Detail & Related papers  (2024-10-28T07:18:32Z)
• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction [88.65168366064061]
  We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference. Our framework leads to a family of three novel objectives that are all simulation-free, and thus scalable. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.
  arXiv  Detail & Related papers  (2024-10-10T17:18:30Z)
• Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models [69.06149482021071]
  We propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives.
  arXiv  Detail & Related papers  (2024-06-20T02:20:23Z)
• Synthetic Face Datasets Generation via Latent Space Exploration from Brownian Identity Diffusion [20.352548473293993]
  Face Recognition (FR) models are trained on large-scale datasets, which have privacy and ethical concerns. Lately, the use of synthetic data to complement or replace genuine data for the training of FR models has been proposed. We introduce a new method, inspired by the physical motion of soft particles subjected to Brownian forces, allowing us to sample identities in a latent space under various constraints. With this in hands, we generate several face datasets and benchmark them by training FR models, showing that data generated with our method exceeds the performance of previously GAN-based datasets and achieves competitive performance with state-of-the-
  arXiv  Detail & Related papers  (2024-04-30T22:32:02Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model [39.363511340878624]
  We present a novel EEG foundation model, namely EEGFormer, pretrained on large-scale compound EEG data. To validate the effectiveness of our model, we extensively evaluate it on various downstream tasks and assess the performance under different transfer settings.
  arXiv  Detail & Related papers  (2024-01-11T17:36:24Z)
• DATGAN: Integrating expert knowledge into deep learning for synthetic tabular data [0.0]
  Synthetic data can be used in various applications, such as correcting bias datasets or replacing scarce original data for simulation purposes. Deep learning models are data-driven and it is difficult to control the generation process. This article presents the Directed Acyclic Tabular GAN ( DATGAN) to address these limitations.
  arXiv  Detail & Related papers  (2022-03-07T16:09:03Z)
• 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)
• Partially Conditioned Generative Adversarial Networks [75.08725392017698]
  Generative Adversarial Networks (GANs) let one synthesise artificial datasets by implicitly modelling the underlying probability distribution of a real-world training dataset. With the introduction of Conditional GANs and their variants, these methods were extended to generating samples conditioned on ancillary information available for each sample within the dataset. In this work, we argue that standard Conditional GANs are not suitable for such a task and propose a new Adversarial Network architecture and training strategy.
  arXiv  Detail & Related papers  (2020-07-06T15:59:28Z)

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.