Mental Tasks EEG Signal Classification Using Support Vector Machine

Wahyu Caesarendra (Faculty of Integrated Technologies, Universiti Brunei Darusslam, Brunei Darussalam)
Syahara U. Lekson (Mechanical Engineering Departement, Faculty of Engineering, Diponegoro University, Indonesia)
Muhammad Agung (Instrumentation Laboratory, Indonesian Institute of Science, Indonesia)

Journal of Infrastructure and Facility Asset Management
Vol 1, No 1 (2019): Journal of Infrastructure & Facility Asset Management

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%.