## 4 Feature Extraction
### Physiological Features  
For controlling motion artifacts, filters were applied to the BVP and EDA signals prior to the physiological feature extraction. Specifically, an order-4 band-pass Butterworth filter was used to filter the BVP signals inside the passband of 1-8 Hz [42]. A Hanning moving average filter was applied to EDA signals with a window of 2.5 s [6]. As physiological signals vary across individuals, we further normalized the signals within each individual participant using z-score normalization [43]. We subsequently extracted a series of generic features from the preprocessed signals, including descriptive statistics of the physiological data (e.g., mean, standard deviation, min, max) as well as mean of the first/second differences of the time-series of BVP, EDA, HR, and TEMP signals on their original and normalized scales. In addition to these generic features, some physiological signal specific features were extracted as well.  
_Electrodermal activity (EDA)_. The EDA signals comprise two components: the slowly changing tonic component, which reflects a person's general skin conductance level (SCL), and the quickly changing phasic component, which is often elicited by external stimuli and is also referred to as the skin conductance response (SCR) [43]. We decomposed the EDA signal into its tonic and phasic component through [PERSON] et al.'s cvxEDA approach [44] and extracted the following features from the two separate components (Table 3). For the tonic component, we computed the mean and standard deviation of the tonic activity level. For the phasic component, we computed the number and the rate of the phasic activity (i.e., the number of SCR per second), the mean and the standard deviation of the amplitude of the SCR peaks, and the mean and standard deviation of the SCR rise time and recovery time as in [45].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Code** & **Description** \\ \hline \hline \multirow{2}{*}{Tonic} & \multirow{2}{*}{SCL} & Mean and standard deviation of the tonic \\  & & skin conductance level. \\ \hline \multirow{8}{*}{Phasic} & SCR\_num & The total number of detected SCRs. \\  & SCR\_rate & The number of SCR per second. \\ \cline{1-1}  & SCR\_peak & Mean and standard deviation of the \\ \cline{1-1}  & \multirow{2}{*}{SCR\_rise\_time} & \multirow{2}{*}{Mean and standard deviation of the time} \\ \cline{1-1}  & & amplitude of the SCR peaks. \\ \cline{1-1}  & & mean and standard deviation of the time \\ \cline{1-1}  & SCR\_resp\_time & intervals between SCR peak and recovery \\ \cline{1-1}  & & point (i.e., 50\% of SCR amplitude). \\ \hline \hline \end{tabular}
\end{table}
Table 3: EDA-specific features.  
_Interbeat interval (IBI)_. To obtain the NN intervals (normal interbeat intervals) from the raw IBI signals, we removed abnormal intervals and ectopic beats based on Malik rules [46] and used linear interpolation to replace the outliers. A series of time- and frequency-domain analysis of NN intervals (NNI) were subsequently performed to measure participants' heart rate variability [47]. Specifically, for time domain features, apart from some descriptive statistics of the NN intervals (e.g., mean, standard deviation, median, range), we computed the SDSD, RMSSD, NN50, pNN50, NN20, pNN20, CVSD, CVNN from the NNI signals as well. Moreover, we also extracted a set of HRV features in the frequency domain, including total power, VLF, LF, HF, LF/HF, LFNU, and HFNU. Details on the computation of HRV features are described in Table 4.