In recording the EEG signals are often contamination signal called artifacts. There are different kinds of artifacts such as power line noise, electromyogram (EMG), electrocardiogram (ECG) and electrooculogram (EOG). This research will compare two methods for removing artifacts, i.e. ICA and PCA methods. EEG signals are recorded on three conditions, which is normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of the EEG signal is obtained in the range of 12-14 Hz (alpha-beta) either on normal conditions, closed eyes, and blinked eyes. From processing with ICA and PCA methods found that ICA method are better than PCA in terms of the separation of the EEG signals from mixed signals.

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) is gaining wide attention as a source of biometric information. Eye-blinks and movement of the eyeballs produce electrical signals (contaminate the EEG signals) that are collectively known as ocular artifacts. These noise signals are required to be separated from the EEG signals to obtain the accurate results. This paper reports an experiment of ocular artifacts elimination from EEG signal using blind source separation algorithm based on independent component analysis and principal component analysis. EEG signals are recorded on three conditions, which are normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of EEG signals in the range of 12-14 Hz either on normal, closed, and blinked eyes conditions is obtained.