Performance Analysis of Random Forest Classifier in Extracting Features from the EEG signal-Reference-Cited by-同舟云学术

Performance Analysis of Random Forest Classifier in Extracting Features from the EEG signal

Published:2022-03-04 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Advanced Computing Technologies and Applications (ICACTA)
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Author:

Jamunadevi C.¹,Ragupathy P.²,Sritha P.³,Pandikumar S.⁴,Deepa S.⁵

Affiliation:

1. Kongu Engineering College,Department of Computer Technology-PG,Erode,India

2. Amrita School of Engineering, Chennai, Amrita Vishwa Vidyapeetham,Department of Computer Science and Engineering,India

3. Bannari Amman Institute of Technology,Department of Electrical and Electronics Engineering,Sathyamangalam,India

4. The American College,Department of Computer Science,Madurai,India

5. Kongu Engineering College,Department of Computer Technology - PG,Erode,Tamilnadu,638060

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9752771/9752788/09753364.pdf?arnumber=9753364

Reference25 articles.

1. An automated system for epilepsy detection using EEG brain signals based on deep learning approach

2. Epileptic Seizures Detection Using Deep Learning Techniques: A Review

3. Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images

4. Automatic seizure detection using three-dimensional CNN based on multi-channel EEG

5. Detecting Epilepsy in EEG Signals Using Time, Frequency and Time Frequency Domain Features;hernández;Computer Science - Theory and Applications,2018

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