Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques
Author:
Affiliation:
1. School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Japan
2. Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
Funder
Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (KAKENHI), Japan
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10091501.pdf?arnumber=10091501
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1. Machine Learning Based Framework for Classification of Children with ADHD and Healthy Controls
2. Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms
3. Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals
4. Feature Enhancement Based on Regular Sparse Model for Planetary Gearbox Fault Diagnosis
5. Accurate Classification of Seizure and Seizure-Free Intervals of Intracranial EEG Signals From Epileptic Patients
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