EEG Signal Classification Using AAR and Resilient Propagation with Emotiv EPOC Device

摘要

Electroencephalogram signals (EEG) has been widely researched and developed in various fields of science. EEG signals can be classified into information for the application of Brain Computer Interface (BCI) topic. There are several approaches in EEG signal classification, but some of these approaches are not robust to the EEG signal that has a lot of artifact and recorded in real time. This study aims to classify the EEG signal to obtain a more optimal result, especially in the EEG signal that has many artifacts and recorded in real time. This research uses the Emotiv EPOC device to record EEG signals in real time. In this research, we propose the combination of Automatic Artifact Removal (AAR) and Resilient Propagation (R_prop) that it has accuracy of 74% in four classes classification and 100% in two classes classification. This proposed AAR can improve 3% to 6% of accuracy.