## 3 Three-Stage Development of BCI-Based Language Decoding
### Stage 3: Intelligent Interaction, Innovation Stage
's work on semantic reconstruction using non-invasive brain recordings [107], which demonstrated that natural language decoders trained on fMRI data can capture semantic meaning across different cognitive tasks, including perceived speech, imagined speech, and even visual narratives. Their approach leverages pre-trained language models and encoding-decoding frameworks to enable more accurate interpretation of neural signals and enhanced semantic modeling. In parallel, [PERSON] et al. introduced another innovative methodology by addressing neural signal non-stationarity through dynamic online learning mechanisms [18], thereby establishing a comprehensive framework that optimizes performance across various operational conditions, while maintaining system stability and reliability. Advances in learning strategies have emerged as another crucial direction in BCI-based language decoding, with novel training approaches and optimization techniques specifically addressing the inherent challenges of neural signal variability and inter-subject differences. Transfer learning methodologies have revolutionized the field by enabling efficient model adaptation across subjects with minimal calibration requirements, substantially reducing the system initialization time, while improving cross-subject generalization [103]. Figure 5: End-to-end architecture for decoding speech from brain signals using wav2 vec 2.0 and contrastive learning (adapted from [14]); wav2 vec: wave-to-vector; CLIP: contrastive language-image pre-training; Conv: convolution. Concurrent developments in regard to continuous learning mechanisms have addressed the fundamental stability-plasticity dilemma in neural networks, enabling progressive knowledge accumulation, while preserving established functionalities [108]. Significant advances in regard to rapid adaptation protocols have emerged through various innovative approaches to neural speech decoding. These developments include hybrid architectures incorporating recurrent neural networks and transformer models, as demonstrated in several studies, including [PERSON] et al. 's work [106], which represents one of the implementations combining deep learning with speech synthesis. In parallel, [PERSON] et al. established new paradigms in continuous learning through their innovative implementation of physical neuromorphic computing [109], representing a significant advancement in continuous knowledge integration, while maintaining system stability. Leveraging these advanced architectural foundations and sophisticated learning strategies, the integration of next-generation functionalities has propelled BCI-based language decoding systems toward increasingly sophisticated and personalized implementations. The convergence of these technological capabilities has enabled the development of more intuitive and responsive communication interfaces that can adapt to individual user needs and environmental contexts. Natural language understanding capabilities have become fundamental to modern BCI systems, with [PERSON] et al. 's neurolinguistic approaches demonstrating significant improvements in direct speech decoding accuracy [110]. This advancement synergizes with enhanced context awareness, as emphasized in [PERSON] et al. 's research on hybrid BCI systems [111], establishing more robust and reliable language interpretation protocols that can adapt to varying communication scenarios. The incorporation of intelligent decision support mechanisms, as investigated by [PERSON] et al., has optimized network structures in regard to bio-inspired computing, significantly enhancing system adaptability across diverse communication contexts, while maintaining processing efficiency [112]. Notable progress in clinical applications has been demonstrated through several recent studies. [PERSON] et al. implemented a chronically implanted BCI system that achieved 80% intelligibility in synthesizing speech from neural signals involving an ALS patient [113].