## 3 Three-Stage Development of BCI-Based Language Decoding
### Stage 2: Dynamic Communication, Advanced Stage
Beyond these technical advances in signal processing and fusion, the expansion of BCI applications has led to unprecedented capabilities in neural signal interpretation and communication, extending beyond basic command-and-control paradigms to sophisticated language processing tasks. This expansion has been particularly transformative in thedomain of language decoding, where advances in neural signal interpretation have enabled the direct decoding of attempted speech and linguistic intent from brain signals. The practical implementation of these advances has been demonstrated through several clinical studies, showcasing significant progress in continuous speech decoding and sentence-level comprehension. A comprehensive clinical evaluation by [PERSON] et al. assessed 25 individuals with advanced ALS, using a P300-based BCI system [97]. Their findings revealed that 72% of participants achieved accuracy rates above 70%, sufficient for basic communication, despite severe physical disabilities. Notably, performance was not correlated with disability severity, with many participants with an ALSF functional rating scale revised score below 5 achieving high accuracy rates, demonstrating the system's potential in regard to severely impaired patients. Another clinical implementation was reported by [PERSON] et al., who developed a \"brain-to-text\" system that successfully decoded continuous speech from intracranial recordings [98]. Testing on seven patients with epilepsy showed word error rates as low as 25% for vocabulary sizes of 100 words. The system demonstrated robust performance, even with limited training data, as little as 25 min of speech was sufficient for accurate decoding. Most recently, [PERSON] et al. achieved a breakthrough in speech synthesis, using neural signals in a clinical setting [99]. Their system, tested on five participants undergoing intracranial monitoring, successfully synthesized intelligible speech by leveraging advanced neural network architectures to decode intended vocal tract movements from brain signals. Notably, the system could synthesize speech, even during silent miming, achieving correlation scores of 0.69 with actual speech patterns, demonstrating its potential for helping patients who cannot physically vocalize. These clinical implementations have collectively demonstrated robust performance across diverse patient populations, reduced training requirements, and successful operation in challenging clinical environments, establishing the practical viability of BCI-based language decoding systems. In parallel with these foundational clinical successes in regard to linguistic advances, the integration of emotion recognition systems has enhanced the sophistication of user interactions, exemplified by [PERSON] et al. 's implementation of functional connectivity networks combined with local activation features for EEG-based emotion recognition, enabling more naturalistic and context-aware communication [100]. Complementary to these developments, [PERSON] and [PERSON] demonstrated the effectiveness of deep learning models in analyzing user sentiments, semantic features, and preferences in terms of product recommendations, underscoring the potential of combining sentiment and semantic analysis with neural decoding systems to further personalize BCI communication platforms and align them with user-specific contexts [101]. Enriching the communication paradigm, the incorporation of contextual information processing, as demonstrated by [PERSON] et al. 's implementation of sentence-level topics in the context of neural machine translation, has further advanced the system's comprehension capabilities by considering broader linguistic and semantic relationships [102]. These developments collectively contribute to more intuitive and expressive BCI-based communication systems. As BCI technology continues to mature, modern systems have achieved significant improvements across multiple domains, including enhanced reliability, reduced calibration requirements, and improved user experience.