# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 4 Data Analysis and Model Development
### Model Evaluation and Comparison  
We then compared the performances of Model 1 (ERP Segment CNN-LSTM) and Model 2 (\(t\)-test-based CNN-LSTM). Model 1, which learns comprehensive spatiotemporal ERP features, and achieved a very high recall (98.76%), rarely missing positive/preferred cases. However, its precision (72.11%) was relatively low, leading to more false positives. Meanwhile, Model 2, using only statistically significant ERP features, improved accuracy (81.90%) and precision (85.00%) but had a lower recall (70.00%).  
Table 20 summarizes each model's strengths and weaknesses. In short, Model 1's extensive coverage of ERP segments yields a higher recall, whereas Model 2's targeted features lead to a better accuracy and precision.  
To further evaluate the generalization performance of Models 1 and 2, a fivefold cross-validation was conducted. Given the relatively limited dataset and intersubject variability in ERP signals, some variation in accuracy was observed across folds. However, Model 2 consistently outperformed Model 1 in terms of classification accuracy. This result supports the notion that extracting meaningful ERP features and feeding them into the CNN-LSTM architecture can lead to a more stable and improved classification performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Advantages** & **Disadvantages** \\ \hline Model 1: ERP Segment CNN-LSTM & \(\bullet\) & Captures comprehensive spatiotemporal features of EEG signals across all ERP segments & \(\bullet\) & Lower precision (72.11\%) due to potential overfitting on irrelevant patterns \\ Model 1: ERP Segment CNN-LSTM & \(\bullet\) & High recall (98.76\%) ensures sensitivity to emotional stimuli, minimizing missed positive responses & \(\bullet\) & Higher computational complexity due to processing all ERP segment features \\  & \(\bullet\) & Suitable for real-time emotion monitoring or attention shift applications & & \\ \hline Model 2: \(t\) Test–based CNN-LSTM & \(\bullet\) & Uses statistically significant features (mean, peak, area under the curve, and peak latency), reducing noise and enhancing efficiency & \(\bullet\) & Lower recall (70.00\%) than Model 1, leading to a higher likelihood of missing subtle positive responses \\ Model 2: \(t\) Test–based CNN-LSTM & \(\bullet\) & High accuracy (81.90\%) and precision (85.00\%) ensure reliable positive predictions & \(\bullet\) & May miss out on valuable spatiotemporal patterns excluded during feature selection \\  & \(\bullet\) & Efficient for scenarios requiring trusted emotional predictions, such as risk management systems & \\ \hline \hline \end{tabular}
\end{table}
Table 20: A comparison of advantages and disadvantages between Model 1 and Model 2.  
Further, a paired \(t\)-test was conducted to statistically compare the cross-validation accuracies of Models 1 and 2. The results revealed a significant difference (\(t=-10.4913\), \(p=0.0005\)), indicating that the ERP feature-based approach of Model 2 is more effective for EEG-based affective classification. These findings suggest that additional data collection and further feature optimization could further enhance model performance.  
These findings validate the feasibility of EEG-based emotion analysis for distinguishing preference versus non-preference in response to architectural stimuli. In practical terms, designers may choose or combine these models based on whether they prioritize sensitivity (high recall) or precision in capturing user preferences. The next section discusses potential real-world applications of these models and their significance for personalized affective architecture.