## 3 Three-Stage Development of BCI-Based Language Decoding
### Stage 1: Signal Interpretation, Basic Stage  
The ICI architecture identifies signal interpretation as the basic stage of BCI-based language decoding (Figure 1), characterized by fundamental approaches focusing on single-channel processing, linear feature extraction methods, and basic classification models. These basic approaches are essential for extracting meaningful linguistic information from brain signals, as language processing involves complex temporal and spatial patterns across multiple brain regions. This initial phase employed basic pattern recognition \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stage** & **Process** & **Key Components** & **Methods/Techniques** \\ \hline
**Signal acquisition** & Raw neural data collection & EEG/ECoG sensors & Multichannel recording \\  & & Recording devices & High sampling rate acquisition \\ \hline
**Preprocessing** & Signal cleaning & \begin{tabular}{c} Artifact removal \\ and enhancement \\ \end{tabular} & \
\begin{tabular}{c} ICA \\ Noise reduction \\ Signal filtering \\ \end{tabular} &
\begin{tabular}{c} ICA \\ Band-pass filtering \\ CSP \\ \end{tabular} \\ \hline
**Feature extraction** & Signal characteristics analysis & \
\begin{tabular}{c} Temporal features \\ Spectral features \\ Spatial features \\ \end{tabular} &
\begin{tabular}{c} Time–frequency analysis \\ Wavelet transform \\ Deep learning (e.g., EEGNet) \\ \end{tabular} \\ \hline
**Decoding** & Neural pattern recognition & \
\begin{tabular}{c} Classification \\ Pattern matching \\ Language modeling \\ \end{tabular} &
\begin{tabular}{c} Machine learning algorithms \\ Deep neural networks \\ wav2 vec 2.0 \\ \end{tabular} \\ \hline
**Output generation** & Language production & \
\begin{tabular}{c} Text generation \\ Speech synthesis \\ Feedback mechanisms \\ \end{tabular} &
\begin{tabular}{c} Language models \\ Speech synthesizers \\ Real-time adaptation \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Brain signal transformation process in regard to EEG/ECoG-based BCI language decoding. Wav2 vec: wave-to-vector; CSP: common spatial pattern; ICA: independent component analysis. techniques with static architectures and offline analysis paradigms, primarily achieving binary classification tasks and simple command recognition within controlled laboratory environments [72; 73]. While limited in terms of complexity, these foundational methods established critical proof-of-concept tests for decoding language-related neural patterns. The applications, though limited in scope, established essential methodological frameworks for subsequent developments in neural signal processing and interpretation, particularly in the context of real-time brain signal analysis and feature extraction methodologies. Building upon this foundational framework, the signal processing architecture in regard to EEG and other brain signal decoding tasks includes single-channel processing, linear feature extraction, traditional classification techniques, and offline analysis paradigms. These components are particularly crucial for detecting and analyzing the temporal dynamics of speech-related neural activities. Single-channel processing facilitates focused signal acquisition from individual sensors, which is particularly valuable in region-specific analysis, as demonstrated by [PERSON] et al. in their investigation of attention markers associated with emotionally laden engagement, through the use of precise temporal resolutions of EEG recordings [74]. Their work utilized inter-subject correlation analysis to identify neural synchronization patterns across multiple participants, establishing a robust methodology for analyzing shared neural responses to naturalistic stimuli. Linear dimensionality reduction methods, notably PCA and ICA, serve to extract critical features, while minimizing noise interference in high-dimensional neural data [75; 76]. These techniques have been proven to be particularly effective in isolating task-relevant neural signatures from background activity and artifacts.