This review, presented by Li et al., details the evolution and the use of brain-machine interfaces (BMIs) as a mode of bidirectional communication between biological neural systems and external devices. Li et al. summarizes key features of the evolution of the BMI technology across different generations such as signal modalities, materials, mechanical properties, operational longevity, and system functionalities. The authors performed a comprehensive literature review, analyzing the evolution of BMI technologies across multiple dimensions, including signal detection methods, material properties, mechanical compatibility, system longevity, and functional capabilities. They synthesized information from different generations of BMI technologies to identify key trends and future directions. Li et al. describe three main paradigms that mark the progression towards more biochemically-modulated BMIs and away from passive monitoring systems through describing three paradigms: 1) the expansion of the signal modality from electrophysiological to biochemical to integrated multimodal detection, 2) the shift from rigid to soft adaptive interfaces, and 3) the evolution of system architecture from open-loop monitoring to closed-loop diagnostic-therapeutic systems. They propose that future BMIs will embody five defining traits: (1) signal homology to ensure biochemical compatibility with natural neural signals, (2) homeostatic regulation through autonomous feedback mechanisms, (3) evolutionary adaptability via self-modifying interfaces, (4) symbiotic bioelectronics that integrate with and grow alongside neural tissue, and (5) bio-hybrid intelligence combining artificial and biological processing for neural augmentation. The overarching goal is to construct more biochemically-modulated systems to allow for a more refined neuromodulation technique that acts as a more intelligent and adaptive form of precision treatment for neurological disorders.