Proceedings Article10.1109/ICOASE51841.2020.9436591
Effective Computational Techniques for Generating Electroencephalogram Data
Mahmoud Elsayed,Kok Swee Sim,Shing Chiang Tan +2 more
- 23 Dec 2020
3
TL;DR: In this article, a number of computational and statistical techniques to generate electroencephalogram data from a previously done experiment on 30 healthy participants experiencing painful stimuli are applied, and they believe this application will benefit the research in the field of biomedical signal processing.
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Abstract: The complexity of the electroencephalogram makes it a significant challenge for physicians and engineers to extract useful information from, process, and classify the electroencephalogram signals. Moreover, the difficulty in conducting clinical experimentation limits the collection of a sufficient number of electroencephalogram data samples for further processing using advanced computational techniques such as deep learning. This complexity and difficulty together with the inflexibility and the subtle linearity of the traditional signal processing techniques motivate us to find innovative techniques to address the problem of insufficient electroencephalogram data. In this paper, a number of computational and statistical techniques to generate electroencephalogram data from a previously done experiment on 30 healthy participants experiencing painful stimuli are applied. We believe this application will benefit the research in the field of biomedical signal processing.
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Citations
Examining the Size of the Latent Space of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps of EEG Frequency Bands
01 Jan 2022
TL;DR: In this paper , a convolutional variational autoencoder (VAE) was used to reduce the dimensionality of EEG data by employing spectral topographic EEG head-maps of different frequency bands.
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SecondOponionNet: A Novel Neural Network Architecture to Detect Coronary Atherosclerosis in Coronary CT Angiography
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Examining the Size of the Latent Space of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps of EEG Frequency Bands
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TL;DR: The empirical results suggest that when VAEs are deployed on spectral topographic maps with shape 32\times 32, deployed for 32 electrodes from 2 seconds cerebral activity, they were capable of reducing the input up to almost 99%, with a latent space of 28 means and standard deviations.
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