Enhancing Decision-making Function Using Brain-computer Interface

Abstract

Brain-computer interfaces (BCIs) are one of the key research topics today, particularly important in the field of neuroscience, and researchers have found that BCIs can be applied not only to stroke or paralyzed patients to allow them brain-computer interfaces are helpful in decision-making, mainly by improving various algorithms or applying strategies to improve the correctness of decisions. This would certainly help to address the uncertainties or major mistakes that occur when people are faced with the choices they make in their daily lives. However, there is a lack of a systematic understanding of the mechanisms behind them and the advantages and disadvantages of various approaches. The main focus of this review is to introduce the basic workflow of BCIs as well as several approaches that have been used in recent years to help improve decision-making (e.g., meta-learning approaches based on transfer learning, collaborative brain-computer interfaces, etc.). The aim is to understand the mechanisms behind them and their advantages and disadvantages. On this basis, a few suggestions are made in order to identify potential possibilities for further research.