Fig. 1
From: Investigating the power of eyes open resting state EEG for assisting in dementia diagnosis
Figures showing the total number of times that features for HC-D (A) and AD-DLB (B) classification. Wrapped feature selection utilised training and testing datasets and was simulated 100 times such that model consistency could be ascertained. In both cases, several features were selected consistently, with other features which were selected less adding redundant information that did not improve classification accuracy. With features consisting of the relative delta, theta, high theta, alpha and delta power in addition to the ration of the high theta-alpha relative power (TAR) dominant frequency (DF), dominant frequency variance (DFV) and the ratio of the dominant frequency variance between the EC and EO state (EC/EO)