Classification of Hand Movements from EEG Signals using Machine Learning Techniques
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Tarih
2019
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Brain-Computer Interface (BCI) is a communication system that enables individuals who cannot use their existing muscular system and nervous system because of various reasons to interact with the outside world. BCI enables the person to communicate with some electronic devices by interpreting brain activities. In this study, seven distinct hand movements were classified using the steady-state visually evoked potential (SSVEP) based BCI. EEG signals, which were recorded by researchers from the Autonomous University, were used. The classification was performed using algorithms of Naive Bayes, Extreme Learning Machine (ELM), and Support Vector Machines (SVM) by applying Autoregressive (AR), Hjorth, and Power Spectral Density (PSD) features which were extracted from EEG signals. As a result, classifier performances are 71-86% for AR features, 60-79% for Hjorth features, and 75-93% for PSD features by regarding feature extraction methods. On the other hand, classifier performances are 69-86% for Naive Bayes, 76-93% for ELM, and 60-82% for SVM by regarding classification algorithms. Among achieved accuracy performances, the best accuracy is 93% when the combination of PSD features and ELM algorithm is used. © 2019 IEEE.
Açıklama
2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019 -- 31 October 2019 through 2 November 2019----156545
Anahtar Kelimeler
Brain-computer interface, Classification., EEG, Steady-state visually evoked potential
Kaynak
Proceedings - 2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019
WoS Q Değeri
Scopus Q Değeri
N/A
