The neurophysiological mechanisms underlying curiosity and intrinsic motivation are currently not well understood. However, being able to identify objectively, from neurophysiological signals, the curiosity level of a user, would bring a very useful tool both to neuroscientists and psychologists, to understand curiosity deeper, as well as to designers of human-computer interaction, in order to trigger curiosity or to adapt an interaction to the curiosity levels of its users. A first step to do that, is to collect neurophysiological signals during known states of curiosity, in order to develop signal processing/machine learning tools to recognize those states from such signals. We propose an experimental protocol, that has been designed but has not been tested so far, in order to measure both brain activity through Electroencephalography (EEG) and physiological responses (heart rate, skin conductance, Electrocardiogram) when subjects are induced into different states of curiosity. During the experiment, fun facts will be presented to subjects to induce different levels of curiosity. We obtained those fun facts using the Google functionality "I’m feeling curious" as well as crowdsourcing. A subject will be able to choose a fun fact that makes him curious, and push forward with a 4-to-10 questions chain on this theme. For each question on a given theme, a subject will be able to reveal the answer (interpreted as a curious state) or to skip it (interpreted as a non-curious state). Skipping an answer will automatically break the chain and will point the subject to the next fun fact. Neurophysiological signals will be collected between a question and the choice of revealing the answer. Then the subject will grade the question on a 1-to-7 curiosity level scale. Neurophysiological measures during these states of curiosity will be recorded and we expect to find biological markers of curiosity by analyzing such information.