## 5 Real-Time Evaluation of HRI with BCI
### Developing BCI Systems for HRI Evaluation and Optimization  
In order to evaluate these psychological effects based on human neural activity in HRI, a passive BCI (as opposed to an active BCI designed for controlling external devices with the user's brain signal) [86] could be utilized. To implement it in a specific HRI scenario, one should first specify the effect of interest in the given task. Although multiple psychological effects might play a role in the same task, in most cases, the target effect would be more focused. For example, researchers might be most interested in measuring the extent of the motor priming effect in a task where a robot and a human are both involved in intricate physical movements. Therefore, the specification of the effect of interest depends on the purpose and type of different HRI tasks and scenarios.  
Once the goal of the BCI system is determined, researchers could identify potential neural signatures associated with the target effect by means of a literature search on related neuroscience studies. Special attention should be paid to the paradigm reported in original studies, as the neural correlates of the desired effect could vary across different paradigms. Pilot studies with a similar setting to the real HRI application, if possible, are recommended to confirm the validity of the chosen neural signatures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Psychological** & **Related Factor** & **Cognitive Processes Involved** & **References** \\
**Effects** & & **(And Their Potential EEG Signatures *)** & \\ \hline \multirow{4}{*}{Social facilitation} & \multirow{4}{*}{Physical presence} & \multicolumn{2}{c}{Arousal level} & [61, 62, 63, 64, 65, 66] \\  & & & (alpha and beta power) & \\  & & & Performance monitoring & \\  & & & (error-related negativity, ERN) & [71, 72, 73] \\ \hline \multirow{2}{*}{Motor priming} & \multirow{2}{*}{Motor action} & \multicolumn{2}{c}{Sensorimotor mirroring} & \multirow{2}{*}{[80, 81]} \\  & & & (event-related mu suppression) & \\ \hline \multirow{4}{*}{Joint action} & \multicolumn{2}{c}{Response conflict} & \multirow{2}{*}{[84, 85]} \\  & & & (N2 component) & \\ \cline{1-1}  & & & Stimulus classification, response discrimination & \\ \cline{1-1}  & & & (P3 component) & \\ \cline{1-1}  & & & Action planning & \\ \cline{1-1}  & & & (contingent negative variation, CNV; & [50, 51] \\ \cline{1-1}  & & & movement-related potential, MRP) & \\ \hline \hline \end{tabular}  
* Note that the EEG signatures of these psychological effects are contingent on specific tasks and cognitive processes involved in HRI.  
\end{table}
Table 1: Psychological effects in social interaction and their potential EEG signatures.  
A typical BCI system consists of multiple component stages such as data acquisition, preprocessing, dimensionality reduction, feature extraction, classification, and application [87]. To construct a BCI based on the selected features, we recommend classical handbooks on BCI application (e.g., [88; 89]) for technical details. The performance of the BCI prototype should then be evaluated in simulated HRI scenarios by comparing the BCI output to other validated measures of the target effect, as well as by evaluating the usability of the system [90]. If the system performance could not meet certain criterions, one might search for other neural signatures or adjust the algorithm of the system modules. An iterative process is often needed in the development of BCI systems.  
The implementation of the BCI in real HRI scenarios enables a real-time evaluation of the effect of interest. The robotic agents could be further designed to adapt to the BCI output in a timely manner to optimize task performance and user experience. The general steps for developing BCIs for HRI evaluation and optimization are schematized in Figure 2.