The PsyIntEC project is currently in the last phase of a two months experiment session covering the following tasks:

• T2 Baseline human factors study, single human worker
• T3 Human factors study: pair of cooperating workers
• T5 Human-robot factors study of turn taking

After the experiment session the data analysis part will begin.

The signals from the biosensors will be used to estimate the emotional state of each participant in each task according to the two-dimensional valence and arousal scale. Valence means the type of emotion ranging from negative (e.g. disgust), neutral and positive (e.g. joy). Arousal means the strength of an emotion ranging from low to high. Each signal is usually a measure of either valence or arousal as discussed below.

If a data set is valid (it may contain errors for various of reasons, for example the participant not understanding the rules of the puzzle task) the first step is to remove the baseline. The baseline is the individual activity in sensors during a relaxed state. The effect of baseline is that increased activity with the same amplitude in two individuals can mean very different strengths of activation depending on the amplitude of the baseline in each individual. After baseline removal internal and external artifacts needs to be removed from the raw signal (for example eye blink, electro-magnetic field) and, in the case of EEG, band pass filtered to extract the relevant frequency bands (mostly alpha and beta bands).

After pre-processing we have the following signals:

• GSR is a good indicator of arousal but also on other cognitive aspects such as alertness and effort. Higher GSR levels have been correlated with high involvement in a task, most likely due to high motivation. There is also some evidence of GSR being a relatively strong indicator of negative emotions (negative valence) but no such correlations has been made for positive emotions.
• Zygomatic EMG increases with positive emotions (valence) and decreases with negative valence. It is a stronger indicator of positive than negative valence.
• Corrugator EMG increases with negative valence and decreases with positive valance. It is a stronger indicator of negative than positive valence (i.e. the opposite of zygomatic EMG).
• ECG. The heart rate will be extracted from the ECG signals by locating and counting the high positive R-waves. Increased heart rate is a good indicator of arousal, especially for negative emotions.
• Alpha band from EEG is increased during relaxing states (neutral, low arousal) and is reduced during activity. There is evidence that for negative emotions there are a relatively stronger alpha activity in the right frontal hemisphere than the left, and vice versa for positive emotions.
• Beta band from EEG is a strong indicator of alertness and concentration in a task and of mental or physical activity. It can be seen as a measurement of arousal since for example high activity and alertness in a task that produces a negative emotion can boost the strength of that emotion.
• Raw EEG can be used to see the mental load of a participant during a task.

The different signals will be averaged in a participant over each task. A task is a Towers of Hanoi game either in single human, human-human, human-robot predictable or human-robot unpredictable setting. Each participant has repeated each setting three times. The average values of the signals will be used to calculate the relative difference in emotional stress in a participant between the different settings, and the average relative differences over all participants will be used to see if there are any noticeable trends. We are especially interested in differences in emotional stress between playing with and without robots.