Using linear discriminant function to detect eyes closing activities through alpha wave

This work presents an alternative method to detect events correlated to eyes opening and closing, based on electroencephalography (EEG) measured from the occipital lobe. The goal is to propose a method based on linear discriminant function to classify segments of EEG signals that contain activities originated by eyes closing. A linear discriminant function presented by Fisher is employed to detect these activities on segments of 2s. This method showed a good values of sensitivity (SE ≥ 85 %) and specificity (SP ≥ 60 %). This approach can be used to control the switching of a brain computer interface (BCI).