## 2 Method
### Analysis
#### 2.4.2 ERP
Familiar), and time points are determined at which the difference in ERP amplitude between self and other familiar's faces reaches significance threshold. Then, the temporal cluster is formed by joining contiguous significant time points, and the test statistics are computed as the sum of \(t\) values within the cluster. The test statistics are computed 1000 times by random permutation, i.e., randomly shuffling the conditions, through which one can obtain a null distribution of test statistics. The probability of obtaining the observed test statistics is computed based on the null distribution obtained by random permutation. [PERSON] and [PERSON] [46] have shown that extracting significant temporal clusters by this procedure allows for sensitive detection of time windows of interest while controlling the family-wise error rate to the expected level. The family-wise error rate was adjusted to \(0.05/3=0.0167\) because there were a total of three comparisons. In addition to ERP waveform, the same comparisons were made for time series of global field power (GFP) [47] computed across all 61 channels by cluster-permutation statistics in the same procedure as described above to find any signs of conditional difference in the global map of scalp EEG patterns. Time series of GFP in each condition was obtained by computing across-sensor standard deviation of ERP amplitude at each time point. Diagonal Decoding in Multivariate Analysis  
Epoched EEG data were entered into multivariate analysis with the support of the Amsterdam Decoding and Modeling Toolbox [34]. The same set of epoched data from 61 ch as used in the conventional ERP mean amplitude analysis was used for multivariate analysis. The procedure of multivariate analysis comprises subject-level analysis and group-level analysis. In subject-level analysis, data were first downsampled to 50 Hz (resulting in 40 time points in each trial). Linear discriminant classifier (LDC) was trained to discriminate trials of self condition from those of familiar condition at each time point. LDC was chosen because it showed superior performance in classification of ERP data compared to other classification algorithms in previous studies [33; 34]. In order to avoid overfitting, cross-validation (CV) was performed by 5-fold CV procedure. In 5-fold CV, one fifth of the trials were used as test data and the remaining trials as training data. Using the training data, LDC was trained at each time point (\(-100\) to \(700\) ms after stimulus onset) to classify self and familiar trials, and its classification performance was quantified using the test data. Every trial was used as test data once during the CV. In this step of CV, termed \"diagonal decoding\" [34], classification performance of LDC trained at time point \(t_{1}\) was tested by the test data at the same time point \(t_{1}\). Based on the performance of test data classification, area under the curve (AUC) of the receiver operator characteristics (ROC) curve was calculated at each time point within the time widow of \(-100\) to \(700\) ms after stimulus onset. Training of LDC was carried out separately for the three Version conditions (Original, Enhanced, and Degraded). Thus, a total of three time series of AUC was obtained for each participant in the subject-level analysis. In the group-level analysis, time series of AUC in each Version condition was tested against chance level (AUC = 0.5) at each time point. Significant temporal cluster was searched for while controlling for the family-wise error rate by the permutation clustering approach with 1000 iterations [46]. Because a total of three comparisons were made, the significance threshold was set to \(0.05/3=0.0167\). The temporal stability of the scalp EEG pattern that differentiates self and familiar condition was tested by temporal generalisation analysis [48; 49]. The same set of epoched data from 61 ch as used in the multivariate analysis was used for temporal generalization analysis.