Gürkan Küçükyıldız, Suat Karakaya
In this study, a brain computer interface (BCI) system was explored. Instead of high cost EEG devices, a low cost commercial EEG device (EMOTIV) was used. Raw EEG data was obtained by using research edition SDK of EMOTIV. EMOTIV EEG device has 14 channels (10-20 placement) for EEG and two channels (x and y axis gyro: GYROX, GYROY) for head movements. Head movements and eye-blink can affect the EEG data and are usually referred to as artifacts. In this study, raw EEG data was pre-processed using the x and the y axis gyro data and the two front EEG channels, namely AF4, F8, in order to determine whether the data is artifact free or not. EEG data was collected from subjects that were asked to accomplish two cognitive tasks: pushing a cube and relaxing. Subjects performed each task for a duration of five seconds during 20 trials. The acquired EEG data was divided into 0.25 second epochs. Epochs that were determined to have artifacts were discarded. Power spectral density (PSD) and time domain based features were extracted from artifact free epochs. The features were then used to train a Support Vector Machine (SVM) to determine the corresponding task. The performance of the SVM classifier was compared to that of an Artifical Neural Network (ANN) based one. Experimental results show the efficacy of the SVM based scheme.