Intelligent Approach Integrating Multiagent Systems and Case-Based Reasoning in Brain-Computer Interface

In this research paper investigates the integration of Multiagent Systems (MAS) and Case-Based Reasoning (CBR) within Brain-Computer Interfaces (BCls). The aim is to enhance decision-making and adaptation in BCls, addressing limitations of standalone approaches. The problem statement revolves around the need for improved BCI performance and adaptability. Methodology involves designing a hybrid MAS-CBR framework and evaluating its performance against traditional BCI systems. MAS enables collaborative decision-making among agents, while CBR facilitates learning from past cases. Integration is achieved by using MAS for agent coordination and CBR for case adaptation. Key findings indicate that the hybrid approach yields improved decision accuracy and adaptation compared to individual systems. The collaborative nature of MAS and the knowledge reuse of CBR synergize effectively, resulting in enhanced BCI performance. This integration showcases the potential to advance BCls beyond their current limitations. Implications of this research are significant, as it offers a novel approach to overcome BCI challenges. The hybrid MAS-CBR framework can be further refined to optimize real-time decision-making and adaptability in various application domains. Ultimately, this research contributes to the evolution of BCls by leveraging the strengths of both MAS and CBR, leading to more efficient and robust brain-computer interaction paradigms.