Before revision:  We extracted both frequency and time domain features from the EEG signals and applied a Support Vector Machine (SVM)for each domain to classify the patients using those extracted features. Additionally, we study the feasibility of the Filter Bank Common Spatial Pattern (FBCSP) algorithm to extract features from the frequency domain to detect dementia.
After revision:  We extracted both frequency and time domain features from the EEG signals and executive function. We investigated these tasks by using statistical analysis on both time and frequency domains of EEG signals from three classes of human subjects: Dementia (DEM), Mild Cognitive Impairment (MCI), and Normal Control (NC). We also further evaluated the classification performances of two features extraction methods: Principal Component Analysis (PCA) and Filter Bank Common Spatial Pattern (FBCSP) algorithm to extract features from the frequency domain to detect dementia.