## 3 Three-Stage Development of BCI-Based Language Decoding
### Stage 1: Signal Interpretation, Basic Stage
Extending beyond these methodological foundations, preliminary applications in regard to BCI-based language decoding primarily focus on binary classification tasks within constrained vocabulary domains. These early implementations laid the groundwork for more sophisticated language decoding systems, by demonstrating the feasibility of extracting linguistic information from neural signals. The widely implemented BCI2000 system exemplifies this approach through its application in regard to standardized two-choice tasks, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Decision** & **Neural** & **Naive** & **k-NN** & **SVM** & **Rule** \\  & **Trees** & **Networks** & **Bayes** & **k-NN** & **SVM** & **Learners** \\ \hline Tolerance to & & & & & & & \\ redundant attributes & & & & & & & \\ \hline Tolerance to highly & & & & & & \\ interdependent & & & & & & \\ attributes & & & & & & \\ (e.g., parity problems) & & & & & & & \\ \hline Dealing with & & & & & & & \\ discrete/binary/ & & & (not discrete) & & (not directly discrete) & & (not discrete) & \\ continuous attributes & & & & & & continuous) \\ \hline Tolerance to noise & & & & & & & \\ \hline Dealing with danger & & & & & & & \\ of overfitting & & & & & & & \\ \hline Attempts in regard to & & & & & & & \\ incremental learning & & & & & & \\ \hline Explanation ability/ & & & & & & & \\ transparency of & & & & & & \\ knowledge/classifications & & & & & & \\ \hline Model parameter & & & & & & & \\ handling & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._demonstrating the feasibility of reliable neural signal classification in controlled settings [82]. This platform incorporates real-time signal processing modules, feature extraction algorithms, and classification methods within a unified framework, enabling the systematic evaluation of different BCI paradigms. Contemporary developments, including EEGNet, have enhanced decoding efficiency within these constrained parameters through optimized neural network architectures, specifically designed for EEG signal processing [63]. The architecture incorporates depthwise and separable convolutions, significantly reducing the number of trainable parameters, while maintaining high classification accuracy across various BCI paradigms. The practical implementation of these foundational approaches has been further validated through several clinical studies focusing on patients with severe motor disabilities. [PERSON] et al. developed a near-infrared spectroscopy (NIRS)-based communication system for amyotrophic lateral sclerosis (ALS) patients in a totally locked-in state (TLS), achieving an accuracy of approximately 80% across 40 subjects through single-channel processing and basic discriminant analysis [83]. Building upon this approach, [PERSON] et al. successfully implemented a similar NIRS-based system for a completely locked-in ALS patient, utilizing single-channel processing and SVM classification, with positive and negative predictive values of 80.9% and 72.9%, respectively [84]. More recently, [PERSON] et al. validated these foundational methods through fMRI studies, demonstrating that even single-feature linear decoding approaches can effectively reconstruct speech-related neural patterns, with an accuracy of approximately 80% [85]. Beyond these clinical applications, the potential of neural signal processing has been further demonstrated in developmental research. The successful application of convolutional neural networks (CNNs) in decoding speech-related information from infant EEG signals demonstrates the potential in regard to fundamental language recognition capabilities, particularly in detecting and classifying basic linguistic features [86].