## 2 Neurocognitive Foundation of Language Decoding
### Computational Approaches  
Computational approaches in regard to BCI-based language decoding comprise three fundamental components: feature extraction, classification algorithms, and performance evaluation. These methodological frameworks systematically transform raw neural signals into interpretable linguistic outputs through multi-stage processing pipelines. The process integrates temporal, spectral, and spatial analyses, followed by both conventional and deep learning-based classification methods, with performance quantified through comprehensive evaluation metrics. This systematic approach ensures robust signal processing and reliable decoding outcomes in real-world BCI applications. In the context of language decoding from neural signals, robust feature extraction methodologies are instrumental in ensuring reliable classification outcomes. These methods are particularly crucial for capturing language-related neural patterns in EEG signals, as different linguistic processes generate distinct spatiotemporal patterns. Spatial filtering techniques, particularly the common spatial pattern (CSP) algorithm, have demonstrated significant efficacy in enhancing single-trial EEG analysis. [PERSON] et al. established that CSP effectively optimizes spatial filters to differentiate mental states, substantially improving motor imagery classification accuracy through adaptive spatial filter optimization [62]. This advancement has been particularly valuable for decoding intended speech and language components from neural signals. The evolution of feature extraction has been further accelerated by deep learning architecture. Notably, [PERSON] et al. introduced EEGNet, a compact convolutional neural network (CNN) specifically engineered for EEG signal processing, which simultaneously extracts temporal and spectral features, while maintaining computational efficiency [63]. EEGNet implements a hierarchical signal transformation pathway that systematically processes neural signals from acquisition to interpretation. The architecture's information transmission process begins with raw EEG input signals and progresses through specialized convolutional layers, each serving a distinct purpose in regard to signal recognition and transformation. This systematic approach ensures comprehensive neural pattern capture, while maintaining signal fidelity throughout the processing pipeline. As illustrated in Figure 4a, EEGNet's architecture implements sequential processing stages: temporal convolution for frequency filtering, depthwise convolution for frequency-specific spatial filtering, and separable convolution for optimal feature map integration. This architecture has proven especially effective in capturing the complex temporal dynamics of language-related neural activities. Furthermore, integrated time-frequency analysis has emerged as a crucial approach for capturing EEG's multidimensional characteristics. In the context of language decoding, this is particularly important as different linguistic features (phonemes, words, and sentences) manifest across various temporal scales. [PERSON] et al. proposed a dual-feature extraction network that effectively leverages both temporal and frequency information, establishing a new paradigm in EEG signal processing through innovative feature fusion mechanisms [64]. For the successful translation of neural signals into linguistic content, the evolution of classification algorithms has become paramount in BCI applications requiring precise neural signal interpretation. These algorithms are specifically designed to map extracted neural features to corresponding linguistic elements, such as phonemes, words, or semantic concepts. As depicted in Figure 4b, the classification pipeline encompasses a comprehensive sequence of processing stages, beginning with essential preprocessing steps, including data standardization, normalization, and outlier handling through the use of the interquartile range and Tukey's fences.