## 3 Results
### NFT Performance in Different Group Domains at Intervention Blocks  
As demonstrated in the previous section, results from EO tasks require a different analysis from EC tasks. Therefore, the tasks were analysed separately, as described in Section 2.9. Regarding the analysis of no-priming (CG) and the priming (EG) results, both groups increased their alpha during EO (voluntarily) and decreased during EC after one session of NFT (Figure 5). These changes were reflected by linear increase and decrease of the power, respectively, matching the results discussed in the previous section at pre- and post-intervention (Figure 4)  
When individual alpha was regressed on EO NFT tasks, 24 out of 30 EG (80%) participants and 17 out of 30 CG (57%) participants were able to linearly increase their alpha, as suggested by positive individual regression slopes. Checking further, considering half of the maximal slope as the threshold (0.80 = 45.83\({}^{\circ}\)) instead of a zero slope, 12 out of 24 (50%) EG participants and 6 out of 17 CG (35%) were able to increase above this slope. One sample \(t\)-tests revealed that regression slopes in the EG (\(t\)(29) = 4.38, \(p\) < 0.001) were significantly larger than zero, while the CG were not (\(t\)(29) = 1.18, \(p\) > 0.1). We also directly compared the slopes between groups. A \(t\)-test revealed a significant difference between the slopes of the EG and CG (\(t\)(58) = -2.10, \(p\) < 0.05). We have similar results for the EC NFT tasks, as 24 out of 30 EG participants and 24 out of 30 CG participants had negative regression slopes. From these, 17 out of 24 EG and 16 out of 24 CG (~70%) participants had greater negative slopes than half the minimal slope (\(-\)0.72 = -41.14\({}^{\circ}\)). One sample \(t\)-tests revealed that regression slopes in the EG (\(t\)(29) = -5.53, \(p\) < 0.00001) and CG (\(t\)(29) = -4.50, \(p\) < 0.001) were significantly smaller than zero. A \(t\)-test revealed no difference between the negative slopes of the EG and CG (\(t\)(58) = 0.62, \(p\) > 0.05). As such, we decided to only verify in EO condition the existence of group domain effects on NF learning apart from the EG and CG. To verify the group domain effects, the first column of Table 3 depicts the HV frequency distribution of each significant domain feature (according to the methodology described in Section 2.9). The subsequent columns represent the average alpha power slope for the HV and LV groups and their \(t\)-test's. Considering the alpha regression slope, in the TG domain,  
Figure 5: \(Z\)-transformed EEG power at intervention blocks. Alpha z-transformed power over the baseline (restBin) and NFT tasks for EO condition and EC at intervention blocks (nft1 and nft2). Three regression slopes are presented separately for CG and EG. Additionally, the regression equations are depicted as well as the regression lines for each group are indicated by thinner lines. The regression slopes at intervention blocks show a significant alpha increase for the EG in the EO condition. In contrast, the EC condition shows a similar downregulation of alpha in both groups. the EG with LV of _\"actuare\"_ (those acting with less awareness) at T0 (pre-intervention) were the most effective on increasing alpha power in the EO condition. For the SG domain, LV of reported _\"vigour\"_ at Bout in the EG led to the most significant EO NFT performance in this domain, followed by HV of _\"decentering\"_ at Bin and HV of _\"tension\"_ changes (as the difference Bout-Bin). While for the CG, LV of _\"fatigue\"_ changes (as the difference Bout-Bin) led to the most significant EO NFT performance, followed by LV _\"confusion\"_ changes. Considering the GSR regression slopes at intervention blocks, the EG participants with LV of _\"scl std\"_ (standard deviation of the tonic baseline skin conductance level), as well as those with LV of _\"scr sumResp\"_ slope (sum of the amplitudes of phasic event skin conductance responses) had better efficacy on increasing alpha power during EO NFT performance.