Brain-computer interfaces (BCI) translate brain signals into artificial output to restore or replace natural central nervous system (CNS) functions. Multiple processes, including sensorimotor integration, decision-making, motor planning, execution, and updating, are involved in any movement. For example, a BCI may be better able to restore naturalistic motor behaviors if it uses signals from multiple brain areas and decodes natural behaviors' cognitive and motor aspects. This review provides an overview of the preliminary information necessary to plan a BCI project focusing on intracortical implants in primates. Since the brain structure and areas of non-human primates (NHP) are similar to humans, exploring the result of NHP studies will eventually benefit human BCI studies. The different types of BCI systems based on the target cortical area, types of signals, and decoding methods will be discussed. In addition, various successful state-of-the-art cases will be reviewed in more detail, focusing on the general algorithm followed in the real-time system. Finally, an outlook for improving the current BCI research studies will be debated.
Keywords: BCI; Brain-computer interfaces; Decision-Making; Decoding Methods; Intention Estimation; Motor Cortex; Prefrontal Cortex.
© Korean Society of Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.