## 4 Discussion  
The transfer entropy (TE) analysis presented here contributes to the findings reported by our group that described the effects of HD-tDCS on postural control. Here we evaluated predictive information between the right and left sides of the WBA, showing a causal relationship from the right side to the left side. Moreover, we showed an effect of the intensity up to 2 mA with the cathodal condition. Therefore, empirical evidence related to the HD-tDCS on postural control over right-hemisphere TPJ is statistically noticeable, as a modulator, in healthy subjects. Previous studies indicate that, with a disturbed bipedal stance in healthy participants [27,28], as well as in post-stroke participants [29], greater WBA is observed. The findings of [PERSON] and [PERSON] [27] showed an increase in the mediolateral amplitude of the CoP, more evident for the unloaded foot. Following similar directions but without an effect of side, [PERSON] et al. [28] highlighted that the mediolateral velocity of the CoP had a linear increase with the WBA. Both studies suggest a decrease in postural stability with more WBA in healthy participants. In people after stroke, [PERSON] and [PERSON] [30,31] reported a moderate association between WBA and large CoP velocities, whereas [PERSON] et al. [32] showed an association between WBA and between-limb synchronization. We have confirmed, in this study, that our HD-tDCS protocol over the right TPJ in healthy individuals is capable of replicating the behavior of post-stroke VV misperception. The cathodal 2 mA stimulation yields less weight discharge to the left leg, resulting in a loss of complexity of the force plate signal caused by a greater discharge in the right leg, i.e., flow of information (Transfer Entropy) from the right side to the left. Several previous studies explored some of the various physiological rhythms of the human body [33, 34, 35, 36]. Among them, we can describe the system responsible for postural control, in which the oscillations in a temporal series of postural stability establish the physiological rhythm relative to this system. Previously, postural oscillations were believed to comprise static and stationary order [37,38]. However, more recent research has proven the nonlinear characteristics of the human posture stability time series [39, 40, 41, 42]. The discriminatory capacity of various entropy methods in the differentiation of two COP paradigms was confronted in falling and non-falling elderly. They evaluated postural control in both groups of elderly with eyes open (EO) and eyes closed (EC). The authors found  
Figure 6: Comparison of the entropy measurements across the different brain stimulation conditions, and the interaction effects between categorical predictors from the adjusted GLMM [26]. The results indicate that our sham protocol would not yield effects in our variable of interest and that the cathode center condition induced a significant effect on the postural control. that Multiscale Entropy (MSE) and Composite Multiscale Entropy (CompMSE) presented the best ability to discriminate between falling and non-falling [43]. The structure of COP trajectories under stroke patients' paretic and non-paretic foot was examined and showed that the COP profiles under the non-paretic foot were the most regular, showing lower sample entropy [44]. When it comes to using nonlinear measures, there is some consensus in the literature showing a decrease in the complexity of the CoP signals in people with diseases [45]. This is suggested by the \"loss of complexity hypothesis\" pointed by [PERSON] and [PERSON], showing a less capable system to adapt to situations in order to execute tasks because of deterioration in health [46, 47]. The literature confirms the entropy analysis as a promising tool to evaluate the postural control of health and individuals with specific conditions. However, none of them analyzed the causal relationships between time series in postural control.