EEG Processing Pipeline

In EEG signal processing, multiple computational procedures are required to accomplish different tasks. For instance, preprocessing algorithms typically involve filtering techniques (such as bandpass filtering using FIR or IIR designs) and artifact removal methods (like ICA decomposition) to clean raw signals and eliminate noise, thereby obtaining high-quality data for subsequent analysis. Feature extraction algorithms then employ techniques like time-domain analysis (mean, variance), frequency-domain analysis (FFT-based power spectral density), or time-frequency analysis (wavelet transform) to extract meaningful patterns from the processed data. These features serve as inputs for downstream research and analytical applications. Furthermore, classification algorithms (such as SVM, LDA, or neural networks) are implemented to categorize and identify patterns in the data, often involving cross-validation and hyperparameter tuning to optimize model performance. Therefore, the selection and implementation of these computational procedures are critical in EEG processing pipelines, as they directly impact the reliability and interpretability of final results and conclusions.