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Emotional rollercoaster: Using virtual reality to explore emotions


The scenery floats past to your left and right, the railway in front of you. Suddenly, there was a fire. The suspense grows. The ride reaches its pinnacle. Only one thing stands between you and the abyss: the abyss. I’m plummeting towards the earth’s depths. Participants in a new research at Leipzig’s Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) saw these sequences from a rollercoaster ride. However, not in the actual world, but in a virtual one, thanks to virtual reality (VR) glasses. The goal of the study was to see what occurs in people’s brains when they are exposed to emotionally stimulating circumstances.

Until date, relatively simplistic studies have been employed to learn how the human brain processes emotions. Participants would be shown photographs of emotional scenarios while their brain activity was recorded. The research was carried out in a controlled laboratory setting so that the outcomes could be compared readily. The simulated circumstances, on the other hand, were seldom emotionally stimulating and were far distant from our everyday experiences. This is due to the fact that emotions are constantly formed by a combination of previous experiences and numerous external factors with which we engage. It is thus crucial to create scenarios that are viewed as authentic as possible when it comes to emotions. Only in this manner can we be certain that the concurrently recorded brain activity is similar to what happens in real life outside of the lab. Here, virtual reality glasses come in handy. Participants may immerse themselves in circumstances in a dynamic and participatory way, experiencing them as near to reality as possible. As a result, emotions are elicited in a more natural manner.

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According to the findings of the present research, the degree to which a person is emotionally aroused may be noticed in a certain kind of rhythmic brain activity known as alpha oscillations. As a result, the greater the arousal, the weaker the oscillation in the observed EEG signal. “The results so support past research from traditional experiments and illustrate that the signals also occur under situations that are closer to real life,” says Simon M. Hofmann, one of the study’s authors. “We were able to anticipate how strongly a person feels about a circumstance using alpha oscillations. Our models discovered which parts of the brain are most crucial for this prediction. The lower the alpha activity recorded here, the more aroused the individual is, roughly speaking “author Felix Klotzsche says.

“These results and methodologies might be applied to actual applications outside basic research in the future,” says author Alberto Mariola. Virtual reality glasses, for example, are becoming more widely employed in psychiatric treatment. Patients’ emotional states might be better understood using neurophysiological data, which could lead to better therapy. During an exposure setting, for example, therapists might receive immediate insight into the present emotional sensation without interrupting the action by asking the patient directly.

The researchers used electroencephalography (EEG) to study these associations, which enabled them to record the participants’ brain waves throughout the virtual rollercoaster ride in order to figure out what occurs in the brain during the ride. In addition, using a film, the individuals were asked to assess how pleased they were during the VR experience. The researchers sought to see whether the subjective feelings experienced throughout the trip were related to the data collected from brain activity. It didn’t matter whether the circumstance was seen as favorable or bad since people’s preferences for rollercoasters differed. It was the intensity of the experience that counted.

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The researchers utilized three different machine learning models to predict the subjective experiences from the EEG data as precisely as possible. The authors demonstrated that the link between EEG data and emotional sensations may be validated in realistic situations using these methodologies.

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