## 3 Three-Stage Development of BCI-Based Language Decoding
### Stage 1: Signal Interpretation, Basic Stage
For classification algorithm selection, [PERSON] provided a comprehensive comparison framework, evaluating 13 key performance dimensions across major algorithms, including decision trees, neural networks, naive bayes, k-NN, SVM, and rule learners [77]. As shown in Table 2, their analysis revealed that while SVMs excel in regard to general accuracy and handling irrelevant attributes, they require longer training time. Neural networks demonstrate strengths in processing highly interdependent attributes and incremental learning, but are less effective when dealing with missing values. These insights provide valuable guidance for choosing appropriate classifiers in regard to brain signal decoding tasks. The offline analysis paradigm, exemplified in [PERSON] et al. 's ECoG studies, enables comprehensive examination of phoneme patterns in both spoken and imagined speech [78], establishing a methodological foundation for neural signal interpretation through detailed post hoc analysis of temporal and spatial patterns. Their research specifically highlighted the importance of high-gamma band activity (70-150 Hz) in encoding phonemic information. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Decision** & **Neural** & **Naïve** & **k-NN** & **SVM** & **Rule** \\  & **Trees** & **Networks** & **Bayes** & **k-NN** & **SVM** & **Learners** \\ \hline Accuracy in general & \(\star\star\) & \(\star\star\star\) & \(\star\star\) & \(\star\star\) & \(\star\star\star\) & \(\star\star\star\) & \(\star\star\) \\ \hline Speed of learning with & & & & & & & \\ respect to number of & & & & & & & \\ attributes and the & & & & & & & \\ number of instances & & & & & & \\ \hline Speed of classification & \(\star\star\star\star\star\) & \(\star\star\star\star\star\) & \(\star\star\star\star\star\) & \(\star\star\star\star\) & \(\star\star\star\star\) \\ \hline Tolerance to & & & & & & & \\ missing values & & & & & & \\ \hline Tolerance to & & & & & & \\ irrelevant attributes & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of machine learning algorithms (adapted from [77]); the stars (\(\star\), \(\star\star\), \(\star\star\), \(\star\star\star\)) indicate performance levels from minimal to optimal. In accordance with these processing methodologies, foundational pattern recognition in EEG-based BCI research predominantly employs static classification techniques for non-dynamic datasets. This approach is particularly effective for decoding discrete linguistic elements (such as phonemes and words) from continuous brain signals, as well as corresponding cognitive and motor actions, as documented in [PERSON] et al. 's comprehensive review of EEG-based BCI paradigms and their implementation in controlled environments [79]. Their analysis encompassed various preprocessing techniques, including spatial filtering methods, such as CSP and Laplacian filtering, which enhance the signal-to-noise ratio and spatial resolution. The implementation of single-task learning protocols and fixed model architectures, while limiting adaptability, ensures computational efficiency and reproducibility in controlled laboratory settings, as highlighted by [PERSON] et al. 's systematic analysis of classification algorithms in BCI applications [80]. Their work specifically addressed the challenges of non-stationarity in EEG signals and proposed adaptive classification strategies to maintain the level of performance over extended usage periods. Fundamental statistical analyses of signal features, as demonstrated by [PERSON] et al. in silent reading and language processing tasks [81], provide essential insights into data distributions and patterns, establishing a robust framework for preliminary BCI applications through rigorous statistical validation methods. Their research employed time-frequency analysis techniques, including short-time Fourier transform (STFT) and wavelet decomposition, to characterize the spectral dynamics of language-related neural activity.