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This review discusses the integration of error-related potentials (ErrPs) in reinforcement learning-based brain-machine interfaces (BMIs), highlighting their potential for improving system performance through error correction and learning. The authors summarize existing literature on the application of ErrPs in non-invasive systems, emphasizing their ability to provide intrinsic feedback without additional cognitive load. The review aims to advance the understanding of ErrPs in reinforcement learning contex

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qwertys1305

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