EEG EYE STATE IDENTIFICATION BASED ON STATISTICAL FEATURES AND COMMON SPATIAL PATTERN

Firing of neurons in the brain created Electroencephalographic (EEG) signals, which applicable for a non-invasive measure of brain functioning. EEG signals is one of the main sources on the implementation of Brain-Computer Interface (BCI) technology. It is a non-muscle communication link between brain and external device, which enable the neurologic patients to interact with the world through the brain signals. The only communication way for a locked-in patient is through the eye muscles. Hence, eyes closed state and eyes open state are selected as the area of research. Besides, common spatial pattern (CSP) is the well-known method for classification algorithm in the BCI field. However, application of CSP in EEG eye state classification considered uncommon as compared to motor imagery classification. Hence, the proposed work aimed to analyse the EEG eye state signal as well as develop an algorithm using statistical-CSP features on the eye state identification.