# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 2 Literature Review
### Theoretical Background of EEG-Based Emotion Analysis
\begin{table}
\begin{tabular}{c c c c} \hline
**ERP** & **Typical Time** & **Main Cognitive Function** & **Architectural Design Relevance** \\ \hline N100 & \(\sim\)100 & Initial attention, stimulus detection & \begin{tabular}{c} Evaluates whether design elements \\ effectively capture attention [46] \\ \end{tabular} \\ \hline N200 & \(\sim\)200 & Negative emotion, conflict detection & \begin{tabular}{c} Helps refine designs to avoid excessive/ \\ conflicting visual stimuli [47] \\ \end{tabular} \\ \hline P300 & \(\sim\)300 & \begin{tabular}{c} Positive emotion, \\ decision-making \\ \end{tabular} & \begin{tabular}{c} Assesses user satisfaction and positive \\ valuation of design aspects [48] \\ \end{tabular} \\ \hline Late positive & \(>\)400 & Sustained positive emotional response & \
\begin{tabular}{c} Identifies design elements that enhance \\ prolonged well-being/comfort [49] \\ \end{tabular} \\ \hline Positive slow wave & \(>\)500 & Prolonged emotional engagement &
\begin{tabular}{c} Determines whether emotionally \\ significant elements maintain interest [50] \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 3: Major event-related potential components. or content dynamically [54; 55; 56]. The wide range of frequency information (delta to gamma) also enables the simultaneous interpretation of various states like stress, relaxation, or concentration [24; 27; 57; 58]. However, EEG is sensitive to noise from eye blinks, electromyographic signals, and electrical interference, and its relatively low spatial resolution complicates pinpointing localized activations in the brain [52; 53]. Moreover, even when exposed to identical stimuli, EEG patterns can vary greatly among individuals, making it challenging to construct generalized models [59]. To overcome these problems, sophisticated preprocessing methods such as independent component analysis or empirical mode decomposition are needed, possibly in tandem with multi-modality approaches (EEG plus functional magnetic resonance imaging) and large-scale data training using deep learning [60; 61]. Table 4 summarizes the key advantages and limitations of EEG-based emotion analysis. Recently, an increasing number of empirical studies in the architectural domain have applied EEG. Table 5 presents representative examples of this research and their key findings. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Aspect** & **Advantage** & **Limitation** \\ \hline Temporal resolution & Millisecond-level measurement of neural activity & Highly sensitive to artifacts from blinking, muscle movements, and electrical interference \\ \hline Spatial resolution & Noninvasive scalp electrodes are relatively easy to set up & Difficult to precisely localize deeper brain regions \\ \hline Objectivity & Less prone to self-report biases since they use direct brain signals & Intersubject variability can reduce generalizability \\ \hline Practical application & Real-time emotion monitoring feasible (e.g., human-computer interaction, and virtual reality) & Requires complex preprocessing (e.g., independent component analysis) to ensure clean signals \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The advantages and disadvantages of electroencephalography-based emotional analysis.