## 3 Material and Methods
### Data Collection and Analysis  
This study collected the participants' identifying outcomes and brainwave histories for tests of AAC graphic symbols largely through the Graphic Symbol Learning System and EEG technology. To mitigate the effect of activities prior to the test on the participants' brainwave changes, all data collecting tests were conducted during kindergarten school hours after the lunch break. In addition, to prevent grading errors and irrelevant factors, the research team members served as evaluators. The assessment was carried out with touch-screen computers with identical specifications and a standardized oral guidance method. Once the participant put on the EEG headset properly, the research team spent 5-10 min talking to the participants casually and establishing a relationship to prevent evaluation errors caused by factors such as unfamiliarity and nervousness. Meanwhile, during this time span, the participant was allowed to become familiar with the clicking and dragging functions of the touch-screen computer. Subsequently, the research team operated the computer and the assessment software to display the assessment menu. The computer recorded the participant's answers and response times for questions containing various target words while the EEG headset and the real-time monitoring system recorded the participant's numerical brainwave data. This study used SPSS as a tool for statistical analysis. The mean and standard deviation were calculated for the accuracy \begin{table}
\begin{tabular}{c c} \hline
**Brainwave** & **Range (Hz)** \\ \hline Delta & 0.1–4 \\ Theta & 4–7 \\ Low Alpha & 8–9 \\ High Alpha & 11–12 \\ Low Beta & 12.5–16 \\ High Beta & 20.5–28 \\ Low Gamma & 25 \\ High Gamma & 100 \\ \hline \end{tabular}
\end{table} 
Table 3: Frequency range of EEG bands. of the participant's graphic symbols identification and for the EEG numerical values representing attention and relaxation parameters. The numerical values were paired up with graphs to illustrate the brainwave changes that reflected the participant's attention. Furthermore, this study conducted a two-way analysis of variance (ANOVA) to compare the effect and variance of the number of AAC graphic symbol components on identifying outcomes and brainwave histories of children with and without developmental delays. The integrated EEG biofeedback function enabled the user to monitor his or her own attention for brainwave data analysis. Once the Graphic Symbol Learning System began running, the system automatically measured the EEG attentional performance as the baseline before the training. Subsequently, various degrees of attention during training were configured in the system from low (Attention: 50) to moderate (Attention: 70) and substantial (Attention: 90). Once the user put on the EEG headset, the EEG monitoring system performed real-time computation using the data measured from the MindWave headset and transmitted through the Bluetooth connection. In addition, the monitoring system calculated and displayed indices such as Pressure Index (PI), Meditation Index (MI), Body Energy Index (BEI), and Comprehensive Index (CI). These indices were calculated through the following equations:  
PI can be converted from MI as follows:  
\[\text{PI}=100\text{ - }\beta\text{ waves.} \tag{1}\]  
The Relaxation Index can be regarded as MI, and BEI can be calculated as follows:  
\[\text{BEI}\ =\ \frac{(\text{High }\theta\text{ }waves+\text{Low }\theta\text{ }waves)}{2} \tag{2}\]  
CI can be calculated as follows:  
\[\text{CI}\ =\ \theta\text{ }waves/\ [\frac{(\text{High }\theta\text{ }waves+\text{Low }\theta\text{ }waves)}{2}] \tag{3}\]  
The Attention Index demonstrated the participant's mental focus or concentration. When the participant was able to control psychological activities in a highly concentrated state, the Attention Index (ranging between 0 and 100) increased. By contrast, mental states such as distraction, fainting, inattention, and anxiety decreased the Attention Index.