## 4 Discussion
We found the discriminative features in ERPs from the aBCI system are traceable, as shown in Figure 4. The features of P300 and N200 using audio story stimulation and the Shadowing Tasks mechanism were more distinct than those using other methods. These results encouraged us to perform a subsequent study of the aBCI system. In addition, to a decline in mental workload, we shift the usual Shadowing Tasks paradigm [14]: instead of loudly repeating the auditory target stimuli, we ask subjects to echo the target stimuli mentally as it occurs [31]. We call this approach, different from those of previous studies, the mental Shadowing Tasks mechanism. This study used twelve audio stories, six recorded by male and six by female voices. The number of audio stories is greater than that of our previous work [14]. According to the post-analysis, we found that the gender of the story voice did not affect the participants' attention or the average accuracies using N2P3 to distinguish the EEG data, as shown in Table 10.  
Most aBCI studies use more than eight electrodes to collect data [17; 31; 38; 41; 47; 48; 62]. To reduce user discomfort, we used only three electrodes, Pz, Cz, and Fz, to sample the user's brainwaves via EEG equipment in our previous work [14]. In this study, referencing [16; 17; 49] and passing through multiple experiments, we selected five electrodes to collect the data: T3, T4, Pz, Cz, and Fz. From Tables 5 and 6, we inferred that all five points are suitable for sampling electrode points. In the study, we invited 24 participants who had never used a BCI system to test our aBCI system. Tables 2-4 show the average accuracies of EEG data from each electrode for each participant. While the average accuracies are better than chance, the average accuracies are not consistent across the five electrodes of each participant, and there are high individual differences among the participants as well. Therefore, we used a neural network for classification and identification to identify the best prediction function for each user to improve the user's accuracy when using the system. Table 7 shows the average accuracies via the NN analysis. NN analysis does raise all accuracies. The average accuracies for most participants using the data from component N2P3 were the highest among the three components via the NN analysis. Further, the best result of confusion matrices is the precision = 80.02%, sensitivity = 76.75%, specificity = 79.89%, and recall = 76.62% [63]. In addition to auditory stimulation and sampling electrodes, another factor affecting the performance of a BCI is the interpretation of the ERPs. This study analyzed the data of each electrode using three ERP components (N2P3, P300, and N200). Which ERP component should the aBCI system use to interpret the data of the user's brainwaves online? Table 8 shows the average accuracies across all participants for each electrode (including the average accuracies obtained by the NN analysis). This analysis showed that the average accuracy using component N2P3 to discriminate is the best. Table 8 also indicates that no matter which electrode is used, as long as the system uses component N2P3 to discriminate the data, we obtain better results. This result is consistent with the results of our vBCI experimental [13]. According to Table 9, the average accuracies of using the three components to discriminate the data from the same electrodes is significantly different. The accuracy using component N2P3 remains the best. Searching for suitable ERP features and algorithms to raise the information transfer rate (ITR) attracts the most activity in BCI research [64]. Based on [PERSON] et al., the stimuli should exist long enough to evoke the sound-evoked potential. However, if the system uses much longer stimulation, the inter-command intervals will be extended, resulting in lower information-transfer-rates (ITR) [38]. Unacceptably low ITR in this study, as shown in Table 13, is one of the items that the subsequent systems need to improve.