Measuring EEG During Social Video Game
Different brain connections are activated during different social interactions. Several research papers study the brain network through studying interdisciplinary approaches and devices, such as EEG, fMRI, etc . We decided to study the brain network based on social interactions. Based on our research, we assess how different personality types contribute to different brain connectivity. We are interested in finding out the connectivity of the brain when it comes to different social interactions. These interactions will be brought about by a social videogame, where brain activity will be measured with an EEG. Our research aims at elucidating which ERP waves occur during social interactions whilst playing the videogame. For the purpose of this research, a pilot study with 12 people will be done first. We will use a 32-channel EEG, with the electrodes distributed according to the 10/20 principle. Each participant will play the social videogame for 20 minutes, where they will have to pass a ball between other players. After each pass of the ball, we will record the ERP. Before starting, the participants will fill in the Edinburgh questionnaire , which is used to make sure that all participants have the same hemisphere dominance. Alongside this, the participants will fill out the personality type questionnaire, the five big personalities , which will be used to determine how different personality types process social information. The collected data will first be pre-processed and then we will extract the ERP signals.
Based on previous research measuring brain activity during social interactions, we expect the presence of the P300 signal, more precisely P3b . Given the personality types of participants, we expect that statistically significant differences will occur. We expect a considerable difference between participants with high and low characteristics on the neuroticism scale that is described in the personality type questionnaire .
We see our limitations mostly in the amount of collected data and the participants’ profiles. During our research we also mainly focused on measuring brain activity with EEG, however for a better analysis, we should employ more interdisciplinary methods. Alongside the EEG, we could also include fMRI, first-person research, etc.
We believe that this research will prove useful since the topic of brain networks is new, applicative, and important in helping us to discover how different types of people deal with various tasks and challenges.
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