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Data scarcity in the brain-computer interface field can be alleviated through the use of generative models, specifically diffusion models.
“A Novel P300-based Brain-Computer Interface Stimulus Presentation Paradigm: Moving beyond Rows and Columns”
G. Townsend et al · 2010
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“Single-Trial Analysis and Classification of ERP Components — A Tutorial”
Benjamin Blankertz et al · 2010
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“Wasserstein Barycenter and Its Application to Texture Mixing”
Julien Rabin, Gabriel Peyr\’e, Julie Delon and Marc Bernot · 2012
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“Gans trained by a two time-scale update rule converge to a local nash equilibrium”
Martin Heusel et al · 2017
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“EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces”
Vernon. Lawhern et al · 2018
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“EEG Dataset and OpenBMI Toolbox for Three BCI Paradigms: An Investigation into BCI Illiteracy”
Min-Ho Lee et al · 2019
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“Score-based generative modeling through stochastic differential equations”
Yang Song et al · 2020
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“Denoising diffusion probabilistic models”
Jonathan Ho, Ajay Jain and Pieter Abbeel · 2020
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“Systemic Racism in EEG Research: Considerations and Potential Solutions”
Tricia Choy, Elizabeth Baker and Katherine Stavropoulos · 2021
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“Diffusion models beat GANs on image synthesis”
Prafulla Dhariwal and Alexander Nichol · 2021
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“Evolving deep learning models for epilepsy diagnosis in data scarcity context: A survey”
Raghdah Aldahr, Munid Alanazi and Mohammad Ilyas · 2022
Cited alongside, same era.
“Classifier-free diffusion guidance”
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
“Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models”
Rongjie Huang et al · 2023
Cited alongside, same era.
“EEG synthetic data generation using probabilistic diffusion models”
Giulio Tosato, Cesare Dalbagno and Francesco Fumagalli · 2023
Cited alongside, same era.
“Generative AI Enables EEG Data Augmentation for Alzheimer’s Disease Detection Via Diffusion Model”
Tong Zhou et al · 2023
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“EEGWave: a Denoising Diffusion Probabilistic Approach for EEG Signal Generation”
Szabolcs Torma and Luca Szegletes · 2023
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“Generating realistic neurophysiological time series with denoising diffusion probabilistic models”
Julius Vetter, Jakob Macke and Richard Gao · 2023
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“Data augmentation for seizure prediction with generative diffusion model”
Kai Shu et al · 2023
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“Generative adversarial networks in EEG analysis: an overview”
Ahmed. Habashi, Ahmed. Azab, Seif Eldawlatly and Gamal. Aly · 2023
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“Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance”
Ce Ju, Reinmar Kobler and Cuntai Guan · 2023
Cited alongside, same era.
“Synthetic Sleep EEG Signal Generation using Latent Diffusion Models”
Bruno Aristimunha et al · 2023
Cited alongside, same era.
“MEDiC: Mitigating EEG Data Scarcity Via Class-Conditioned Diffusion Model”
Gulshan Sharma, Abhinav Dhall and Ramanathan Subramanian · 2023
Cited alongside, same era.
Nour Neifar, Afef Mdhaffar, Achraf Ben-Hamadou and Mohamed Jmaiel · 2023
Later among the works it cites.
“Mother of all BCI Benchmarks”, 2023
Bruno Aristimunha et al · 2023
Later among the works it cites.
“DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG”
Yilin Wang et al · 2024
Closest in time.