## 4 Key Challenges and Development Trends
### Technical Bottlenecks  
The development of BCI systems, particularly for language decoding applications, encounters several critical technical bottlenecks, including signal quality enhancement, real-time optimization, interference resistance, and system integration.  
Signal quality represents a fundamental challenge in regard to BCI systems, especially when decoding complex neural patterns associated with speech. Neural recordings are inherently susceptible to various artifacts, including physiological activities and environmental interference, which significantly impact decoding accuracy [129; 130]. Recent advances in signal processing have introduced sophisticated methods for artifact identification and removal in regard to multifractal signals, marking substantial progress in terms of EEG signal quality enhancement [131]. This advancement is particularly crucial for maintaining reliable decoding performance in practical applications where signal contamination is prevalent.  
The real-time processing capability presents another critical challenge, particularly in regard to speech and language decoding applications, where minimal latency is essential for natural communication. While high-resolution neural recordings, such as intracranial EEG and ECoG, have demonstrated improved decoding accuracy [121], the challenge lies in processing these complex signals efficiently. The requirement for simultaneous processing of multiple data channels, while maintaining both speed and accuracy, remains a significant technical hurdle [132; 133].  
Interference resistance constitutes a crucial aspect of BCI system development. In language decoding applications, the challenge is particularly acute as speech-related neural signals must be distinguished from concurrent cognitive processes and motor activities associated with natural communication. The ability to discriminate between relevant neural signals and various forms of interference, both external and physiological, is essential. Recent research has highlighted the potential of advanced signal processing and machine learning techniques in regard to non-invasive brain recordings for speech perception decoding, emphasizing their role in interference mitigation [14]. These developments are instrumental in ensuring robust system performance across diverse operational environments.  
System integration represents the culmination of these technical challenges, requiring seamless coordination between hardware components and software algorithms. The complexity of language decoding demands an integrated approach where signal acquisition, processing, and interpretation work in concert to extract meaningful linguistic content from neural signals. Earlier research has analyzed the integration of language models with BCI classifiers, exemplifying the necessity of combining sophisticated neural signal processing with predictive modeling to enhance communication systems [134]. This foundational work continues to influence current approaches in the field. Addressing these technical bottlenecks necessitates continued advancement in regard to signal processing algorithms, hardware optimization, and machine learning models for neural signal interpretation. The progressive resolution of these challenges will facilitate the development of more efficient, real-time BCI systems, suitable for both clinical and non-clinical applications.