Statistical analysis and classification of EEG-based attention network task using optimized feature selection

This research incorporates optimized feature selection using an inheritable bi-objective combinatorial genetic algorithm (IBCGA) and mathematic modeling for classification and analysis of electroencephalography (EEG) based attention network. It consists of two parts. 1) We first design the attention network experiments, record the EEG signals of subjects from NeuronScan instrument, and filter noise from the EEG data. We use alerting scores, orienting scores, and conflict scores to serve as the efficiency evaluation of the attention network. 2) Based on an intelligent evolutionary algorithm as the core technique, we analyze the large-scale EEG data, identify a set of important frequency-channel factors, and establish mathematical models for within-subject, across-subject and leave-one-subject-out evaluation using a global optimization approach. The results of using 10 subjects show that the average classification accuracy of independent test in the within-subject case is 86.51%, the accuracy of the across-subject case is 68.44%, and the accuracy of the leave-one-subject-out case is 54.33%.