## 3 Three-Stage Development of BCI-Based Language Decoding
### Stage 3: Intelligent Interaction, Innovation Stage  
The intelligent interaction stage represents the culmination of the ICI architecture's evolution in regard to BCI-based language decoding, characterized by sophisticated neural decoding systems that achieve efficient brain-to-text conversion through integrated architecture and adaptive learning capabilities (Figure 1). By integrating advanced deep learning frameworks with end-to-end neural signal processing, this stage establishes a synergistic network ecosystem that operates through transfer learning and continuous adaptation mechanisms. Technological advancement manifests through three key dimensions: intelligent architecture integration, sophisticated learning strategies, and next-generation capabilities. Together, these elements drive the transformation from conventional signal-processing approaches toward autonomous, context-aware systems that excel in regard to dynamic adaptation and personalized interactions. To realize this vision of intelligent interaction and establish the foundation for advanced neural decoding, the architectural framework of modern BCI-based language decoding systems has evolved through the systematic integration of complementary technological innovations. The implementation of contemporary deep learning frameworks, particularly PyTorch, has fundamentally transformed neural decoder development by introducing dynamic computational graphs and imperative programming paradigms, establishing a robust foundation for advanced decoding algorithms [104]. This sophisticated technical infrastructure has catalyzed the emergence of multi-task collaborative learning in regard to EEG-based neural decoding, where shared representations across linguistic tasks significantly enhance decoder robustness and the generalization capacity, as extensively documented by [PERSON] et al. [105]. Building upon these technological foundations, the integration of advanced linguistic frameworks has marked another significant milestone in BCI-based language decoding systems. Modern architectures now incorporate sophisticated linguistic processing layers that seamlessly integrate phonological, syntactic, and semantic analysis, enabling a more nuanced understanding of neural signals associated with language processing. This advancement is particularly evident in regard to the recent breakthrough by [PERSON] et al., who developed a novel end-to-end framework that leverages interpretable acoustic parameters to bridge neural activity and speech production [106]. Their approach combines an ECoG decoder with a differentiable speech synthesizer, demonstrating how careful parameter design at multiple linguistic levels, from fundamental frequency to formant structures, can enable accurate and natural speech reconstruction across a large cohort of 48 participants. While [PERSON] et al. 'swork demonstrates the potential of invasive ECoG recordings, parallel breakthroughs have been achieved in regard to non-invasive BCI systems, as shown through developments in end-to-end systems, exemplified by [PERSON] et al. 's innovative approach to direct speech perception decoding using non-invasive brain recordings [14]. As shown in Figure 5, they proposed an end-to-end architecture that combines wave-to-vector (wav2 vec) 2.0, a pre-trained speech module, with a specialized brain module. The system utilizes contrastive language-image pre-training (CLIP) loss to align brain activity representations with speech features, enabling zero-shot decoding of previously unseen speech segments, a significant advancement over traditional classification approaches. [PERSON] et al. 's work demonstrated the potential of using pre-trained speech models for non-invasive brain decoding. While their approach focused on speech perception, subsequent research has explored different directions to advance BCI systems, including [PERSON] et al.