Title :
Improving classification accuracy of EEG based brain computer interface signals
Author_Institution :
Elektrik-Elektron. Muhendisligi Bolumu, Karadeniz Teknik Univ., Trabzon, Turkey
Abstract :
Feature extraction is a very crucial step at modern electroencephalogram (EEG) based brain computer interface system. Various feature extraction techniques have been proposed in order to represent EEG signals. With this study, it was shown that the classification accuracy increased by extracting features from different time segment of EEG signals. The proposed method improved the average classification accuracy to 69.08% which was 65.35% at the previous study.
Keywords :
brain-computer interfaces; electroencephalography; feature extraction; medical signal processing; signal classification; signal representation; EEG based brain computer interface signals; EEG signal representation; EEG signal time segment; classification accuracy; electroencephalogram based brain computer interface system; feature extraction; Electroencephalography; EEG; brain computer interface; feature extraction; imroving classification accuracy;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2015 23th
Conference_Location :
Malatya
DOI :
10.1109/SIU.2015.7130442
