An Adaptive Neurofeedback Method for Attention Regulation Based on the Internet of Things

ABSTRACT

The rapid development of the COVID-19 pandemic has threatened the lives of people around the world. Many people were caught in anxiety and panic, which also prevents people from fully concentrating on their normal lives. However, the current common neurofeedback therapies used to solve the problem of lack of attention cannot fully deal with the differences in each individual. In addition, direct contact between the patient and the doctor also increases the risk of virus transmission during treatment. This article combines neurofeedback and IoT to establish an adaptive attention adjustment method. IoT connects patients and doctors remotely, reducing the direct contact between them. In order to adapt to individual differences, the feedback indicators of each individual are individually calibrated. In addition, the proportional, integral, and derivative controller was used to adjust the difficulty of the feedback task to adapt to each individual's self-regulation ability and provide the individual with a higher level of regulation. We also designed adaptive attention adjustment experiments for different individuals. The results show that through adaptive feedback training, the individual's feedback indicator has dropped by 77.90%, and the individual can adjust his attention state to the individual's optimal baseline threshold, and the oscillation error gradually reduces to the expected threshold range. This method can cope with the differences between different individuals and provide each individual with the same level of feedback regulation. In the future, this study may provide a general adjuvant treatment for other mental illnesses.