# Impact of Sound and Image Features in ASMR on Emotional and Physiological Responses
## 2 Materials and Methods
### Methods
#### 2.2.3 Analysis  
Data analysis was performed using IBM SPSS Statistics 21. The sound and image characteristics of the ASMR videos were thoroughly examined based on the data extracted from the 8 selected videos, with no missing data. EEG data from 30 participants (excluding one outlier) were included in the analysis. Subjective evaluations and ECG measurements were analyzed with complete data from all 31 participants.  
Subjective evaluations were analyzed using the Mann-Whitney U test. Non-parametric tests were applied to the 5-point scale ratings for positivity and relaxation to determine significant differences across the quadrants. Additionally, the emotional vocabulary survey results were analyzed using a cross-tabulation method.  
During preprocessing, the EEG and ECG data were normalized by calculating the difference between the baseline (pre-stimulus) values and the values recorded after the stimulus presentation. EEG and ECG data for each variable were sorted in ascending order, and the top and bottom 10% of the data were excluded to focus on the central 80% of the data. This approach aimed to reduce errors due to data dispersion by removing extreme values. The remaining data were averaged for each corresponding video segment. The primary focus of the analysis was on investigating the average changes in power density values across frequency bands based on electrode positions. Each variable was statistically analyzed using independent sample \(t\)-tests and Mann-Whitney U tests to compare group means.  
Sound and image features of the ASMR videos were extracted using Python (v3.9.10). The sound power spectrum was extracted using the Numpy (v1.24.0) libraries, while mel-frequency cepstral coefficients (MFCCs) were extracted using the Librosa (v0.10.0) library. Image features were extracted using the OpenCV (cv2) library. The features from the 8 ASMR videos were grouped according to specific quadrants. Independent sample \(t\)-tests and Mann-Whitney U tests were employed to calculate and analyze the mean values of each group. This process helped to identify significant differences between the groups.  
The emotion recognition in ASMR videos was also conducted using Python, analyzing a dataset of 56 videos selected by 11 experts. From this dataset, eight videos--representing high valence-low relaxation (HVLR) and low valence-high relaxation (LVHR) emotions--were identified based on their distinct sound and image features. The likelihood of a video being categorized as HVLR or LVHR was determined through statistical analysis. This analysis used the mean and the standard deviation ranges for each group. A total of 26 relevant features were extracted from the sound and image data. Each emotion within the feature value range was initially assigned a probability of 30%. The remaining 70% was distributed as weighted values, based on the distance ratio. The distance between the median of the emotional range and the feature value was calculated as a ratio, normalized to 70%, and added to the base score. Emotion probabilities were computed for all 26 variables, and the emotion with the highest probability was selected as the output.