## 1 Introduction  
Language processing, as a fundamental component of human higher cognition, serves as the cornerstone of complex thought and interpersonal communication [1, 2]. The recent integration of neuroscience and artificial intelligence (AI) has transformed our understanding of neural language implementation, revealing remarkable parallels between human neural representations and computational models in language processing [3, 4, 5]. This scientific advancement has catalyzed the emergence of brain-computer interface (BCI) technologies for language decoding, establishing direct communication pathways between the central nervous system and computational systems [6, 7, 8].  
The evolution of BCI-based language decoding has demonstrated remarkable progress in terms of both technological advancement and practical applications, particularly through the integration of linguistic theories and cognitive frameworks. The synergy between neuroscience, computational linguistics, and artificial intelligence has fundamentally transformed our understanding of neural language processing mechanisms, enabling more sophisticated and naturalistic decoding approaches. This integration has particularly benefited from advances in cognitive neuroscience and computational linguistics, leading to more robust and adaptable systems capable of handling complex language tasks. Technically, the field has transitioned from single-signal decoding to sophisticated multimodal integration, incorporating diverse neural and physiological inputs [9; 10]. Contemporary intelligent algorithms, particularly machine learning, and deep learning approaches have substantially enhanced the precision and efficiency of neural signal interpretation [11; 12; 13]. This technological maturation manifests in two primary BCI categories: non-invasive systems, particularly electroencephalography (EEG)-based interfaces, have exhibited considerable potential in regard to device control and communication facilitation [14; 15], while invasive BCIs have achieved unprecedented breakthroughs in high-speed text and speech communication through precise motor cortex neural activity interpretation [6; 8; 16]. These advancements have facilitated a paradigm shift from fundamental research toward clinical applications, particularly in regard to rehabilitation support and assistive technologies for individuals with neuromuscular disorders [7; 17].  
Despite extensive research in this domain, existing reviews have predominantly focused on isolated aspects, namely neural encoding mechanisms [18; 19], specific language decoding paradigms [20], clinical applications [7; 17], or specific BCI modalities [21], while lacking a comprehensive evolutionary perspective that integrates cognitive mechanisms with technological implementations. While these focused reviews have provided valuable insights in regard to the respective areas, a broader integrative perspective would enhance our understanding of the crucial synergistic relationships between cognitive mechanisms and technological implementations, thereby advancing both our theoretical understanding and the potential for practical innovations in regard to BCI-based language decoding systems. To address this gap, we propose the Interpretation-Communication-Interaction (ICI) architecture, a novel three-stage evolutionary perspective for BCI-based language decoding (Figure 1). This architecture incorporates signal interpretation (basic stage), dynamic communication (advanced stage), and intelligent interaction (innovative stage), synthesizing cognitive neuroscience with computational methodologies. The subsequent sections examine the neurocognitive foundations of language decoding, analyze technological evolution through the ICI architecture lens, and explore future challenges and developmental trajectories in regard to BCI-based language decoding.