# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 3 Materials and Methods  
### Experimental Setup  
EEG data were used to quantitatively analyze user preferences and emotional responses to architectural stimuli. We employed an Enobio 32 (Neuroelectrics, Barcelona, Spain)--a 32-channel wireless EEG system--and NIC software (NIC v2.1.3.11) for precise, reproducible data collection. Figure 2 shows the EEG device with channel positions and the NIC software platform.  
The Enobio 32 noninvasively measures high-density neural signals at a 24-bit resolution [112; 113]. It supports a 500 Hz sampling rate and covers key EEG frequency bands (delta, theta, alpha, beta, and gamma) [113; 114]. Its electrode placement follows the international 10-20 standard, which includes frontal (Fz, F3, and F4), parietal (Pz, P3, and P4), and occipital regions, all closely tied to emotion and cognition [7; 113; 112; 115].  
The NIC software provides real-time EEG visualization, time-frequency analysis, and 3D brainwave mapping [112; 114; 116; 117]. Data are stored in multiple formats (e.g., CSV and EDF). A 60 Hz line filter and visual bandpass filtering from 1 to 50 Hz were applied to minimize noise, focusing especially on alpha and beta bands, which are strongly associated with emotional responses [26; 118; 119; 120]. Additionally, the wireless setup lets participants move more naturally, reducing external signal interference and user anxiety, thus improving data reliability [112; 121].  
For architectural stimuli, we used Python 3.9 and the google-images-search library (\"[[https://pypi.org/project/Google-Images-Search/](https://pypi.org/project/Google-Images-Search/)]([https://pypi.org/project/Google-Images-Search/](https://pypi.org/project/Google-Images-Search/)) (accessed on 25 January 2025)\") to collect images of 25 architectural styles (e.g., modern, contemporary, and rustic) [122]. All images had a minimum resolution of 1920 \(\times\) 1080 pixels and were standardized for brightness, color, and contrast to minimize visual discrepancies [123; 124].  
During the experiment, each image was presented in a random order on a monitor for 5 s, followed by a 2 s rest interval to reduce participant fatigue and clearly capture EEG responses (ERP) [13; 125]. After viewing each image, participants indicated their response--like (1), neutral (2), or dislike (3)--via a keyboard input, which was synchronized with the EEG signal [126].  
The NIC software's event markers align image-presentation timing and participant-response timing with the EEG signal [127; 128]. This enables precise mapping between each image's preference rating and the corresponding EEG pattern. EEG and event data were stored in various file formats (.easy,.edf, and.nedf), ensuring a broad compatibility with different analytical tools. The header files contain participant IDs, the experiment date and time, channel information, and event information that precisely matches stimulus onsets and responses.  
Figure 2: Hardware and software: (**a**) Enobio 32 (medically certified electroencephalography device); (**b**) the electrode position; and the (**c**) NIC desktop.