Revolutionary Deep Learning Method to Denoise EEG Brainwaves

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=eb89JQm53JY


Duration: 5:14
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5-min ML Paper Challenge
Presenter: https://www.linkedin.com/in/albert-lai-b90655171/

Denoising Time Series Data Using Asymmetric Generative Adversarial Networks
https://link.springer.com/chapter/10.1007/978-3-319-93040-4_23

Denoising data is a preprocessing step for several time series mining algorithms. This step is especially important if the noise in data originates from diverse sources. Consequently, it is commonly used in biomedical applications that use Electroencephalography (EEG) data. In EEG data noise can occur due to ocular, muscular and cardiac activities. In this paper, we explicitly learn to remove noise from time series data without assuming a prior distribution of noise. We propose an online, fully automated, end-to-end system for denoising time series data. Our model for denoising time series is trained using unpaired training corpora and does not need information about the source of the noise or how it is manifested in the time series. We propose a new architecture called AsymmetricGAN that uses a generative adversarial network for denoising time series data. To analyze our approach, we create a synthetic dataset that is easy to visualize and interpret. We also evaluate and show the effectiveness of our approach on an existing EEG dataset.



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Tags:
deep learning
machine learning
brain computer interfaces
artificial intelligence
EEG
generative adversial network