## 3 Three-Stage Development of BCI-Based Language Decoding
### Evolution Pattern Analysis and Comparison  
The evolution patterns of BCI-based language decoding demonstrate systematic progression across technical, performance, and application dimensions, reflecting the transformative advancement in neural communication technologies. Through comprehensive analysis of evolutionary trajectories, this investigation reveals the intricate interplay between technological innovation, performance optimization, and practical implementation in regard to neural decoding systems. The technical evolution trajectory exhibits multidimensional progression, characterized by increasingly sophisticated methodological approaches. Rather than discrete jumps between stages, the field demonstrates continuous advancement through transitional phases. The progression from basic to advanced stages was marked by a gradual evolution in both signal processing and computational frameworks, exemplified by [PERSON] et al. 's implementation of parallel processing and sophisticated feature selection through stepwise linear discriminant analysis (SWLDA) [115], and [PERSON] et al. 's expansion from single to multi-phoneme classification, with advanced 8 \(\times\) 8 electrode configurations [116]. In regard to signal processing, the field has witnessed a decisive transition from rudimentary single-modality methods through multimodal integration to advanced intelligent fusion systems, enabling substantially more robust language decoding performance [117]. This methodological advancement parallels the evolution in algorithmic architectures, progressing from conventional machine learning paradigms through hybrid computational models to sophisticated deep learning frameworks, optimized for neural signal interpretation [118]. The transition to innovative stages has been characterized by architectural advancement, demonstrated by [PERSON] et al. 's introduction of encoder-decoder frameworks [119] and [PERSON] et al. 's development of semantic reconstruction approaches [107]. The synergistic development of signal processing and algorithmic innovations has catalyzed the transformation of practical applications, evolving from fundamental offline analysis to real-time processing capabilities and, ultimately, to intelligent interactive systems. Contemporary biomimetic computer-to-brain communication systems exemplify this progression, achieving unprecedented naturalistic sensory feedback, through intelligent neural signal processing and interpretation [120]. System characteristics have evolved correspondingly, transcending the initial confined laboratory applications to become increasingly universal and, ultimately, personalized, with adaptive neural speech decoding frameworks demonstrating remarkable capabilities in regard to individual user customization and optimization [106]. Performance metrics have demonstrated distinct developmental trajectories throughout multiple critical parameters across the three ICI stages (Table 3), establishing new benchmarks in regard to neural communication efficiency. These improvements reflect gradual transitions, rather than sudden breakthroughs. [PERSON] et al. 's standardization of recording protocols with 8- and 16-channel montages exemplified a crucial intermediate step in signal processing maturation [97]. Subsequently, [PERSON] et al. 's implementation of high-resolution micro-ECoG recordings demonstrated the progressive enhancement of spatial resolution and signal quality [121]. Fundamentally, despite increasing task complexity across the different stages, the decoding performance has maintained its stability. As shown in Table 3, the signal interpretation stage achieved a consistent level of performance (80-100%) in basic binary tasks, while dynamic communication expanded to complex speech tasks and, in doing so, maintained considerable accuracy (25-98% accuracy). Notably, intelligent interaction demonstrated enhanced stability (45.6-91.8%), even as it advanced to sophisticated natural communication tasks.