Identifikasi Aktivitas Otak Berdasarkan Pola Sinyal EEG (Electroencephalogram) Menggunakan Model SVM (Support
Vector Machine) dan Fungsi Kernel
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
Setiawan, Edy
Yosrita, Efy
Aziza, Rosida Nur
Yosrita, Efy
Aziza, Rosida Nur
Subject
Teknik Informatika
Datestamp
2023-06-20 02:33:07
Abstract :
Research on word classification based on EEG signals has been carried out by (Rauf, 2021). dataset used in these studies is a dataset obtained from recording EEG signals for the words eat, drink, hungry, thirsty, happy, sad, sick, and toilet, the eight words were recorded using an emotive with five conditions namely, relax, look at pictures, read aloud, read silently, and imagine. Based on the research suggestion that has been done previously, it is to use the SVM classification method to identify brain activity based on EEG signal patterns to explore the SVM method. The results of the study are 6 classification models, namely the results of the accuracy rate using the RBF kernel Mean feature of 29%, Standard Deviation feature of 19%, and the Mean and Standard Deviation feature of 19%, and using the Sigmoid kernel Mean feature of 20%, Standard feature Deviation is 18%, and the Mean feature and Standard Deviation feature is 20%, from the results of the 6 models made in this study, the highest level of accuracy is obtained by the SVM model with the RBF kernel of 29% with the Mean feature.
Research on word classification based on EEG signals has been carried out by (Rauf, 2021). dataset used in these studies is a dataset obtained from recording EEG signals for the words eat, drink, hungry, thirsty, happy, sad, sick, and toilet, the eight words were recorded using an emotive with five conditions namely, relax, look at pictures, read aloud, read silently, and imagine. Based on the research suggestion that has been done previously, it is to use the SVM classification method to identify brain activity based on EEG signal patterns to explore the SVM method. The results of the study are 6 classification models, namely the results of the accuracy rate using the RBF kernel Mean feature of 29%, Standard Deviation feature of 19%, and the Mean and Standard Deviation feature of 19%, and using the Sigmoid kernel Mean feature of 20%, Standard feature Deviation is 18%, and the Mean feature and Standard Deviation feature is 20%, from the results of the 6 models made in this study, the highest level of accuracy is obtained by the SVM model with the RBF kernel of 29% with the Mean feature.
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