## 3 Three-Stage Development of BCI-Based Language Decoding
### Evolution Pattern Analysis and Comparison
Technologies, such as high-density micro-electrocorticographic (uECoG) recordings, exemplify these improvements, in regard to enhanced spatial resolution and signal quality [121]. In parallel, temporal processing capabilities have advanced in regard to neural decoding systems. As shown in Table 3,latencies improved from 3 to 26 s in signal interpretation's basic tasks to 2.64-10.23 ms in dynamic communication's speech decoding. Despite handling more complex natural language tasks, intelligent interaction maintains efficient performance at 50-235 ms. Recent implementations using spiking neural networks (SNNs) have demonstrated the potential for further optimization, achieving sub-10 ms processing, with high computational efficiency [122]. Correspondingly, system stability has improved substantially in regard to neural decoding systems. As shown in Table 3, performance consistency evolved from single-session stability in signal interpretation to cross-session reliability in dynamic communication, with accuracy maintained at 96.8% across the sessions. The intelligent interaction stage further achieved consistent performance (PCC > 0.8) across 25 subjects. In particular, long-term stability has been demonstrated in intracortical BCIs, which maintain robust recording capabilities for up to 3 years in non-human primates and 5 years in human subjects subject to controlled conditions [18]. Likewise, user adaptation requirements have substantially evolved. As shown in Table 3, the training duration was reduced from 5 to 7 weeks in regard to the signal interpretation stage to 20-107 min in regard to the dynamic communication stage. The intelligent interaction stage shows varied adaptation periods from 15 min to 6 weeks, reflecting different task complexities. Recent automatic calibration methods have achieved efficient parameter updates at 2-5 s intervals [109]. In parallel with these technical and performance improvements, the scope of application has expanded considerably, validating and exceeding the ICI architecture's evolutionary predictions. BCI systems have evolved from basic communication aids to sophisticated comprehensive solutions integrating advanced speech decoding and synthesis capabilities, facilitating increasingly natural communication modalities [7]. The user base has diversified significantly beyond the initial constraints, as evidenced by extensive clinical trials of implantable BCIs, extending beyond specialized research subjects to encompass diverse patient populations with varying communication needs and capabilities [128]. The functional complexity has progressed systematically from basic neural signal decoding to sophisticated mechanistic models incorporating multiple neural representations and processing pathways [117], while the practical value has increased substantially through enhanced reliability and effectiveness in real-world applications across diverse environmental conditions [13]. The systematic analysis of these evolutionary trajectories reveals several significant emerging trends that warrant careful consideration in terms of future development efforts. First, the accelerating convergence of technical capabilities and performance metrics suggests an imminent breakthrough in practical application feasibility, potentially leading to widespread clinical implementation. Second, the increasing emphasis on personalization and adaptability indicates a fundamental shift towards user-centric design paradigms, necessitating more sophisticated adaptive algorithms and interface solutions. Third, the expanding scope of application demonstrates the technology's potential beyond traditional medical applications to broader communication contexts, suggesting possible paradigm shifts in human-machine interactions. Fourth, the integration of advanced machine learning techniques with neurophysiological insights indicates a trend toward more biologically informed computational models.