Automatic Classification and Artifacts Removal of EEG Signal Using Supervised and Unsupervised Model-Reference-Cited by-同舟云学术

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Automatic Classification and Artifacts Removal of EEG Signal Using Supervised and Unsupervised Model

Published:2023-10-18 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)
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Author:

Gururaj Bharathi¹,Nair Prajith Prakash¹,L Harish²,S Arun Kumar³,Sharma Manish⁴,Maranan Ramya⁵

Affiliation:

1. ACS College of Engineering,ECE,Karnataka,India

2. CMR Institute of Technology,Information Science and Engineering,Karnataka,India

3. Presidency University,Department of Computer Science and Engineering,Bengaluru,India

4. Graphic Era Deemed to be University,Department of Computer Science & Engineering,Dehradun

5. Saveetha School of Engineering, SIMATS,Department of Research and Innovation,Chennai,Tamil Nadu,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10330805/10331621/10331743.pdf?arnumber=10331743

Reference14 articles.

1. Unsupervised Feature Learning for Outlier Detection with Stacked Convolutional Autoencoders, Siamese Networks and Wasserstein Autoencoders: Application to Epilepsy Detection

2. Unsupervised Seizure Detection based on Rhythmical Activity and Spike Detection in EEG Signals

3. Towards automatic detection of movement during sleep in pediatric patients with epilepsy by means of video recordings and the optical flow algorithm

4. U-EEG: A Deep Learning Autoencoder for the Detection of Ocular Artifact in EEG Signal

5. Model-based Controller Design for Automatic Two Wheeled Self Balancing Robot

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