Step 1. Same as for the EDA cross correlation Step 1 (see Box 2). 2. Analyze the data. Since this study aims to validate the wearable signals against a RD, the phasic activity coming from classical trough-to-peak analysis (TTP) was reported (threshold for an SCR amplitude was set at .01 μS) (Boucsein, 2012). The data were analyzed with Ledalab; therefore, the default settings for filtering and smoothing from the program were used (Benedek & Kaernbach, 2010). Note that different choices in filtering and smoothing can influence the results. 3. Retrieve the parameters from the timeframe determined. Three parameters from the EDA data are evaluated with a Bland–Altman plot:   Mean skin conductance level (SCL) The skin conductance level was based on the whole signal (start baseline – baseline after the noise task). The mean was calculated by averaging over the complete signal. Biological plausible values for SCL is between 0 and 16 μS (Braithwaite, Watson, Jones, & Rowe, 2013), the boundaries of the Bland–Altman plot are therefore ± 1.6 SC.   Number of SCRs The SCRs in the signal were determined through trough to peak (TTP). The number of SCRs per minute was then determined. Biological plausible values for number of SCRs are on average 1–3 per minute according to (Braithwaite et al., 2013) and during high arousal 20-25 per minute (Boucsein, 2012), the boundaries of the Bland–Altman plot are therefore ± 2.5 SCRs.   SCRs total amplitude (S-AMPL) The amplitude of a response was determined as the difference in conductance between response onset and response peak. The amplitudes were added in order to determine the total amplitude. The total amplitude is therefore a function of both the number of SCRs and the amplitude of all these SCRs. Biologically plausible values for amplitudes are between 0 and 3 μS and on average 0.30–1.30 μS according to (Braithwaite et al., 2013) and with 20–25 SCRs per minute the range of total amplitudes is between 0 and 0.3*20 = 6 μS when using the most conservative values. The boundaries of the Bland–Altman are therefore ± 0.6 μS. 4. Check for normality and missing data. The assumption of the Bland–Altman is that the differences between the wearable and the RD are normally distributed. Therefore normality of the differences needs to be assessed visually. If the data appears not normal appropriate transformations (e.g. log transformations) can be used as suggested by Boucsein (2012). Additionally, the quantity of missing data can be viewed from these plots. If the amount of missing data is effecting the power, then inferences should be made with more caution or possibly no inferences can be made. 5. Create a Bland–Altman plot. Plot the mean of the two measurements as the abscissa (x-axis) value, and the difference between the two values as the ordinate (y-axis) value. $\left(\frac{E4+\mathit{TT}}{2},E4-\mathit{TT}\right)$ Additionally plot the two proposed boundaries and the 95% CI of the differences in a different color. Calculate the amount of data outside the CI, as follows: $\frac{\mathit{\text{count}}\left({\mu }_{\left(E4,\mathit{TT}\right)}-1.96{\sigma }_{\left(E4,\mathit{TT}\right)}>E4-\mathit{TT}>{\mu }_{\left(E4,\mathit{TT}\right)}+1.96{\sigma }_{\left(E4,\mathit{TT}\right)}\right)}{n}\ast 100$