## 3 Three-Stage Development of BCI-Based Language Decoding
### Stage 2: Dynamic Communication, Advanced Stage  
The advanced stage of BCI-based language decoding demonstrates significant technological maturation through enhanced real-time processing architectures and multimodal fusion frameworks (Figure 1). These improvements enable the development of sophisticated dynamic communication platforms, leading to expanded applications and second-generation breakthroughs in regard to system performance and clinical implementation. Building upon fundamental BCI signal acquisition and processing capabilities, enhanced real-time processing frameworks emerged as fundamental components for advanced system performance, marking a transition from traditional offline analysis to dynamic platforms. These advancements are particularly crucial for language decoding tasks, where real-time processing of complex linguistic neural patterns requires sophisticated computational architectures. The implemented frameworks incorporate multichannel parallel processing architectures, enabling simultaneous analysis of distributed neural signals across cortical regions to capture comprehensive brain activity patterns [91]. The implementation of dynamic feature extraction mechanisms, particularly advanced time-frequency analysis techniques, such as wavelet transform and adaptive spectral analysis, facilitates the identification of temporal variations in neural signals and accommodates the non-stationary nature of brain activity [92]. Furthermore, the integration of adaptive classification algorithms, notably the adaptive k-NN and dynamic SVM, addresses individual variability and signal fluctuations, thereby optimizing classification performance in dynamic conditions [93]. Building upon these algorithmic innovations, the incorporation of online learning mechanisms enables continuous system adaptation through real-time data-driven model updates, significantly enhancing the system's interactive capabilities and reducing the need for frequent calibration procedures. Along with the evolution of signal processing techniques, the development of multimodal fusion frameworks marked a crucial advancement in BCI system architecture, representing a shift from single-modality approaches to integrated multi-source processing systems. This integration is especially vital for language decoding, as speech and language processing involve distributed neural networks that generate signals across multiple modalities, from motor cortex activity to semantic processing areas. These frameworks systematically integrate complementary information from multiple neural recording modalities, such as combined EEG-functional near-infrared spectroscopy (fNIRS) systems, substantially improving the performance of these frameworks across various cognitive and motor tasks, through the exploitation of both hemodynamic and electrophysiological signals [94]. To enhance the integration efficiency of the system, the implementation of cross-modal feature learning techniques, particularly deep learning architectures, including multimodal autoencoders and attention mechanisms, facilitates the extraction of shared representations from heterogeneous data sources, while preserving modality-specific characteristics. Notable contributions include the emotion recognition system proposed by [PERSON] et al., which employs dynamic weight adjustment across modalities to optimize the predictive accuracy and maintain system robustness in conditions with varying signal quality [95]. Moreover, collaborative enhancement mechanisms have been successfully implemented in clinical applications, such as in regard to Mild Cognitive Impairment (MCI) identification, where temporal and spatial features are integrated through adaptive dynamic functional connectivity analysis [96], demonstrating the practical utility of multimodal approaches in diagnostic settings.