Independent Components of EEG in Moral Judgment

Moral judgment can be defined by the individual s performance, according to the norms and values of society. To better understand morality, researches interest in the investigation of EEG during moral judgment.Independent Component Analysis (ICA) has significant advantages in the analysis of independent components of EEG data. Also, Group ICA analysis and clustering identifies the homogeneous components of the participants and makes it possible to compare them together or with group inference.Method The EEG signal of fourteen healthy participants was registered during a moral judgment task. Four types of dilemmas were considered: impersonal, easy personal, difficult personal and control [1]. The EEG signals were recorded in 32 channels and with a sampling rate of 512 Hz. After preprocessing, each person s data was epoched into 4 categories based on types of dilemmas. ICA was performed on each of 4 categories besides the whole epochs. Then the independent components of the aggregate data were considered as the center for clustering of the independent components of the other 4 categories. The clusters with the most members (maximum 5 and minimum 3 members) were examine.Results Figure 1 shows the resulted brain component cluster with 5 members. The properties of components have been investigated in Figure 2. The power spectrum follows the 1/f pattern and also has little activity in the alpha band. This cluster shows activity in the frontal area and can be considered as a component of brain activity (not an artifact one).Conclusions There was activity in the frontal area during moral judgment, which is in line with the results of Green’s study [2].