The first game-based treatment for children with attention-deficit hyperactivity disorder (ADHD) was approved by the United States Food and Drug Administration (FDA) in 2020. This game was developed for use at home as part of everyday training and can be used along with one’s usual training plan. In this game, two tasks are performed in parallel: (1) a perceptual discrimination targeting task (response and not response and avoiding responding to sudden pop-up targets) and (2) a sensory-motor navigation task (players continuously adjust their location to interact with or avoid positional targets). However, the brain activity of people playing this game was not examined, and the immersive environment (3D virtual world) was not considered. Therefore, we aimed to develop a system to investigate brain activity using electroencephalography (EEG) during multitask gameplay in virtual reality (VR). In this experiment, we focused on the difference between the success and failure of the Go/No-Go task in a multitask game. We created a color discrimination task and a target tracking task in VR. The content of this game task was designed using previous multitask training. EEG and event data were recorded. Using event data, we can analyze the data in detail. We divided the trial types (Go and No-Go) and results (success and failure). We then compared the success and failure of each task. In the Go trial, the relative theta power in success at Fz was significantly higher than that of failure. However, no difference in power was observed in the No-Go trial. On the other hand, theta power was no different between success and failure in the other task. These results of the Go trial suggest that the participants were attentive to processing both tasks. Thus, it is possible that theta power in the frontal area 1 s before stimulation could predict the success or failure of the Go trial. On the other hand, the results of the No-Go trial may be due to the low number of No-Go failure trials and the fact that stimulus oversight is one of the factors for success.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Artificial Intelligence