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Muhammad Agung
Instrumentation Laboratory, Indonesian Institute of Science, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Library & Information Science Mathematics Transportation

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Mental Tasks EEG Signal Classification Using Support Vector Machine Wahyu Caesarendra; Syahara U. Lekson; Muhammad Agung
Journal of Infrastructure & Facility Asset Management Vol 1, No 1 (2019): Journal of Infrastructure & Facility Asset Management
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1292.068 KB) | DOI: 10.12962/jifam.v1i1.5231

Abstract

This paper presents a result of electroencephalography (EEG) signal classification for mental tasks such as thinking forward, backward, left, and right. The EEG data in this study were recorded from Emotive device with 14 channels and 2 references. The aim of this study is to identify the most sensitive channels to the mental task classification. Prior to feature extraction, the EEG signal were decomposed using wavelet with three level decomposition. Eighteen features were extracted from the processed data. Principal component analysis (PCA) is then used to reduce 18 features into 3 principal components. The principal component were classified using support vector machine (SVM). The results show that the SVM classification accuracy of 75%.
Co-Authors Syahara U. Lekson Wahyu Caesarendra