EEG Signal Processing Workflow

EEG (Electroencephalography) processing serves as a fundamental component in neuroscience and brain-computer interface research, with the primary objective of extracting meaningful information from raw brain signals. The entire pipeline typically consists of two major stages: preprocessing and feature extraction, each requiring specific technical methodologies.

### Preprocessing Stage

Due to EEG signals' susceptibility to various noise artifacts, preprocessing constitutes an essential step. Common noise sources include ocular artifacts (EOG), muscle activity (EMG), power line interference, and instrumental baseline drift. Key preprocessing techniques involve:

Filtering: Implementation of bandpass filters (e.g., 0.5-40Hz) to eliminate high-frequency noise and low-frequency baseline drift using functions like butterworth filters in signal processing libraries. Denoising: Application of Independent Component Analysis (ICA) through algorithms such as FastICA or wavelet transform techniques (e.g., using PyWavelets) to remove physiological artifacts. Rereferencing: Computational reassignment of reference electrodes using average reference or specific montages to reduce channel-specific biases. Epoching: Segmenting continuous signals into time-locked epochs based on experimental design, particularly crucial for Event-Related Potential (ERP) analysis around stimulus events.

### Feature Extraction Stage

Preprocessed EEG signals require further transformation to facilitate machine learning or statistical analysis. Prominent feature extraction methods include:

Time-domain features: Computational extraction of statistical measures like mean amplitude, variance, and peak-to-peak amplitude, particularly valuable for ERP analysis through simple array operations. Frequency-domain features: Spectral analysis using Fast Fourier Transform (FFT) implementations or wavelet transforms to extract power in specific bands (e.g., alpha: 8-13Hz, beta: 13-30Hz). Time-frequency analysis: Application of Short-Time Fourier Transform (STFT) with sliding windows or Continuous Wavelet Transform (CWT) to capture dynamic spectral changes using libraries like SciPy. Spatial features: Implementation of Common Spatial Patterns (CSP) algorithm for enhancing classification performance in task-specific paradigms like motor imagery through covariance matrix optimization.

EEG processing involves sophisticated mathematical and programming techniques where appropriate selection of preprocessing and feature extraction methods directly impacts analytical outcomes. While end-to-end feature learning through deep learning architectures (e.g., CNNs, LSTMs) represents an emerging trend, traditional methods maintain widespread adoption due to their interpretability and well-established theoretical foundations.