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Journals / Ejovoc (Electronic Journal of Vocational Colleges) / 2018 / Cilt: 8 - Sayı: 2

A STATISTICAL FEATURE EXTRACTION IN WAVELET DOMAIN FOR MOVEMENT CLASSIFICATION: A CASE STUDY FOR EYES OPEN, EYES CLOSED, AND OPEN/CLOSED FIST TASKS

Şükriye KARA
Semih ERGİN
Pages
158–162
Publication Date
2018-11-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The best result is obtained by using k-NN.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Analysis of brain signals constitute an importance, especially for paralyzed people or people suffer from motor disabilities.
📊
Key Findings & Data:For this aim, some studies have been evaluated to measure signals from the scalp to provide non-muscle control arguments.
💡
Conclusion & Impact:The best result is obtained by using k-NN.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:EEG · electroencephalogram · wavelet transform

Abstract

Analysis of brain signals constitute an importance, especially for paralyzed people or people suffer from motor disabilities. For this aim, some studies have been evaluated to measure signals from the scalp to provide non-muscle control arguments. Brain-Computer Interface Systems turns these signals into device signals that are controllable at the level of thought. In this paper, we classify diverse tasks according to EEG (electroencephalogram) signals. Then pre-processing, feature extraction and classification steps are hold. For classification, we use FLDA, Linear SVM, Quadratic SVM, PCA, and k-NN methods. The best result is obtained by using k-NN.

Özet

Analysis of brain signals constitute an importance, especially for paralyzed people or people suffer from motor disabilities. For this aim, some studies have been evaluated to measure signals from the scalp to provide non-muscle control arguments. Brain-Computer Interface Systems turns these signals into device signals that are controllable at the level of thought. In this paper, we classify diverse tasks according to EEG (electroencephalogram) signals. Then pre-processing, feature extraction and classification steps are hold. For classification, we use FLDA, Linear SVM, Quadratic SVM, PCA, and k-NN methods. The best result is obtained by using k-NN.

🔖

Cite This Article

KARA, Ş., ERGİN, S. (2018). A STATISTICAL FEATURE EXTRACTION IN WAVELET DOMAIN FOR MOVEMENT CLASSIFICATION: A CASE STUDY FOR EYES OPEN, EYES CLOSED, AND OPEN/CLOSED FIST TASKS. Ejovoc (Electronic Journal of Vocational Colleges), Cilt: 8 - Sayı: 2, 158–162. https://ajindex.com/articles/252711

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