# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 4 Data Analysis and Model Development
### Model 2: CNN-LSTM with Significant Features (\(t\)-Test)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ERP Segment** & **Feature** & **Definition and Importance** \\ \hline \multirow{4}{*}{N100} & \multirow{4}{*}{Mean} & The average signal value in the ERP segment, reflecting the baseline response strength to a stimulus. Higher mean values indicate stronger emotional stimuli, aiding in assessing initial sensory responses \\ \cline{2-4}  & \multirow{2}{*}{Peak} & Highest amplitude in the ERP segment, reflecting the immediate emotional or attentional intensity. Higher peak values for N100 denote stronger initial responses, useful for evaluating response intensity \\ \cline{2-4}  & \multirow{2}{*}{Area under the curve (AUC)} & Cumulative strength of the response over time within the ERP window. At N100, it assesses the duration of early neural responses, capturing initial reactions to emotional stimuli \\ \hline \multirow{4}{*}{N200} & \multirow{4}{*}{Mean} & The average ERP amplitude reflecting baseline response strength. At N200, it indicates an attention shift or mismatch detection—key for identifying stimuli causing cognitive conflict \\ \cline{2-4}  & \multirow{2}{*}{AUC} & Representations sustained cognitive processing over time. For N200, it evaluates the strength and duration of an individual’s response to stimuli in conflict or requiring heightened attention \\ \hline \multirow{4}{*}{P300} & \multirow{2}{*}{Peak latency} & The time from stimulus onset to maximum amplitude. For P300, shorter latencies imply a higher emotional salience and stronger cognitive evaluation of stimulus importance \\ \cline{2-4}  & \multirow{2}{*}{AUC} & The total amplitude across the P300 window. Reflects how much cognitive resource is allocated, highlighting sustained engagement with an important stimulus \\ \hline \multirow{4}{*}{Late positive potential (LPP)} & \multirow{2}{*}{Peak latency} & The time taken to reach peak amplitude within the LPP window. Used to gauge the speed of emotional response, helping clarify early emotional assessments \\ \cline{2-4}  & \multirow{2}{*}{AUC} & Cumulative amplitude from stimulus onset through the LPP period, indicating how long and how intensely emotional processing is maintained over time \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Merged event-related potential (ERP) feature definitions and segment-specific importance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Channel** & \ \begin{tabular}{c} **Event-Related** \\ **Potential Segment** \\ \end{tabular}  & **Feature** & **\(t\)-Statistic** & \(p\)**-Value** \\ \hline FP1 & N100 & Mean & \(-2.468\) & \(0.0137\) \\ \hline FP1 & N100 & Peak & \(-2.487\) & \(0.0130\) \\ \hline FP1 & N100 & \begin{tabular}{c} Area under the curve (AUC) \\ \end{tabular}  & \(-2.470\) & \(0.0136\) \\ \hline FP1 & N200 & Mean & \(-2.444\) & \(0.0146\) \\ \hline FP1 & N200 & AUC & \(-2.444\) & \(0.0146\) \\ \hline F3 & P300 & Peak latency & \(-2.275\) & \(0.0230\) \\ \hline F3 & LPP & Peak latency & \(-2.3  
Using an 80-20 train-validation split and EarlyStopping (stopping within 50 epochs), the final model achieved the following:  
* Accuracy: 81.90%;
* Precision: 85.00%;
* Recall: 70.00%;
* F1 score: 73.00%. By focusing on statistically significant ERP features, Model 2 achieved an improved accuracy and precision. However, its recall was somewhat lower, indicating subtle positive responses may have been missed. Still, these outcomes provide a robust neuroscientific foundation for designing affective architectural spaces.