Study on Accuracy Improvement of Mental Arithmetic Task Classification Using Different Classifiers with DWT Feature Extraction Method-Reference-Cited by-同舟云学术

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Study on Accuracy Improvement of Mental Arithmetic Task Classification Using Different Classifiers with DWT Feature Extraction Method

Published:2023-02-23 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Electrical, Computer and Communication Engineering (ECCE)
language:
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Author:

Touhid Tanvir Ibn¹,Anam Mahbub¹,Alam Mohammad Rafiqul¹,Foysal Mahir²,Shaiham Shibly³

Affiliation:

1. Chittagong University of Engineering and Technology,Department of EEE,Chittagong,Bangladesh

2. University of Houston,Department of Electrical and Computer Engineering,Houston,USA

3. Chittagong University of Engineering and Technology,Department of CSE,Chittagong,Bangladesh

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10101485/10100742/10101596.pdf?arnumber=10101596

Reference11 articles.

1. New Machine Learning Algorithm: Random Forest

2. The Ability to Predict Seizure Onset

3. Ensemble Learning

4. Single-trial classification of antagonistic oxyhemoglobin responses during mental arithmetic

5. Single trial classification of fNIRS-based brain-computer interface mental arithmetic data: A comparison between different classifiers

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: A systematic review;Expert Systems with Applications;2024-09

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