# Color Texture Image Complexity--EEG-Sensed Human Brain Perception vs. Computed Measures
## 7 Experimental Results
### EEG Experiment Analysis
#### 7.3.2 Fractal Images  
The effect of complexity over the ERPs, in relation to CE, CFD, and participants scoring is also observed, in parts, for the \(Frac\) images, as shown in the following. ERPs vs. CE  
CE complexity levels distributions: low: 6.1%; medium: 14.1%; and, high: 79.8% (unbalanced). Relating with CE values, few cases are detected as significantly anti-/correlated for ERPs:  
- participant P1: Ch P3, N300, \(\rho=-0.25\); P300, \(\rho=-0.21\); 2 nd P3b \(\rho=-0.25\); Ch P4, P300, \(\rho=-0.26\); 2 nd P3b \(\rho=-0.27\)  
- participant P4: Ch P3, N300, \(\rho=0.34\); 2 nd P3b \(\rho=0.25\); Ch P4, P200, \(\rho=0.29\); 2 nd P3b \(\rho=0.24\)  
- participant P6: Ch P3, P300, \(\rho=0.21\)  
ERPs vs. CFD  
CFD complexity levels: low: 41.4%; medium: 14.1%; high: 44.5% (unbalanced). In relation with the CFD levels, as shown in Figure 11 for the P3 channel, ERPs are significantly correlated on grand average for P300 with \(\rho=0.21\) and 2 nd P3b \(\rho=0.2\), and for two participants at the P3 and P4 channels, as shown below:  
- participant P4: Ch P3, N300, \(\rho=0.34\); 2 nd P3b \(\rho=0.27\); Ch P4, N300, \(\rho=0.26\); 2 nd P3b \(\rho=0.22\)  
- participant P8: Ch P3, P300, \(\rho=0.2\); Ch P4, P300, \(\rho=0.26\)  
On grand average, the ERPs cannot be easily differentiated, as seen in Figure 11. Here, the high complexity level only induces a 1 \(\upmu\)V higher ERP, as compared to low and medium classes. The right plot shown in Figure 11 as for P4 channel, can be further seen in extension, in the top plot of Figure 12 which considers in addition, the spatial activity. Here, spatial activity shows an interesting phenomena at the 900-1000 ms time point, where the medium class has higher activation when compared to the high class. However, this effect is not maintained within the neighboring intervals. Figure 10: GA Spectrum for \(Nat\) images on 3 complexity levels according to CFD, at T4 channel. Spectral Activity vs. Participants Scoring participants complexity levels distributions: - P1: low: 30.3%; medium: 41.4%; high: 28.3% (\(\sim\)balanced). - P2: low: 4%; medium: 87.9%; high: 8.1% (unbalanced). - P3: low: 0%; medium: 63.6%; high: 36.4% (unbalanced). - P4: low: 25.3%; medium: 30.3%; high: 44.4% (\(\sim\)balanced). - P5: low: 81.8%; medium: 10.1%; high: 8.1% (unbalanced). - P6: low: 61.6%; medium: 25.3%; high: 13.1% (unbalanced). - P7: low: 0%; medium: 100%; high: 0% (unbalanced). - P8: low: 1%; medium: 13.1%; high: 85.9% (unbalanced). Investigating the power spectrum in relation with subjective scoring (Figure 13), we observe an increase in power with an increased level of complexity from 20 Hz onwards. However, no significant correlations are observed. Figure 11: GA ERP for _Frac_ on 3 complexity levels according to CFD, for P3 and P4 channels. Figure 12: GA ERP for _Frac_ images on three complexity levels according to CFD. GA ERP temporal evolution at channel P4 (top plot) and GA ERP spatial evolution at different timings (bottom scalp plots). Overall, more significant positive correlations were detected for participants in relation to the _Nat_ textures. Participants P3 and P8 show positive correlations in neural signals for _Nat_ cases, while P4 and P8 exhibit positive correlations for _Frac_ cases. The highest correlations detected in relation to CE and, respectively, CFD for participant P4 for the _Frac_ case are supported by the complexity criteria used in the thought processes that refer solely to texture characteristics, namely: colors, distribution, and spatial arrangement (disorder vs. order), which are expressed by CE and, respectively, CFD analysis measures.