Please use this identifier to cite or link to this item: https://repository.unej.ac.id/xmlui/handle/123456789/100651
Title: Multilayer Extreme Learning Machine for Hand Movement Prediction Based on Electroencephalography
Authors: ANAM, Khairul
AVIAN, Cries
NUH, Muhammad
Keywords: Classification methods
Electroencephalography
Extreme learning machine
Movement prediction
Multilayer ELM
Issue Date: 1-Dec-2020
Publisher: Bulletin of Electrical Engineering and Informatics, Vol. 9, No. 6, December 2020, pp. 2404~2410
Abstract: Brain computer interface (BCI) technology connects humans with machines via electroencephalography (EEG). The mechanism of BCI is pattern recognition, which proceeds by feature extraction and classification. Various feature extraction and classification methods can differentiate human motor movements, especially those of the hand. Combinations of these methods can greatly improve the accuracy of the results. This article explores the performances of nine feature-extraction types computed by a multilayer extreme learning machine (ML-ELM). The proposed method was tested on different numbers of EEG channels and different ML-ELM structures. Moreover, the performance of ML-ELM was compared with those of ELM, Support Vector Machine and Naive Bayes in classifying real and imaginary hand movements in offline mode. The ML-ELM with discrete wavelet transform (DWT) as feature extraction outperformed the other classification methods with highest accuracy 0.98. So, the authors also found that the structures influenced the accuracy of ML-ELM for different task, feature extraction used and channel used.
URI: http://repository.unej.ac.id/handle/123456789/100651
Appears in Collections:LSP-Jurnal Ilmiah Dosen

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