EEG Processing Project with MATLAB

Introduction to EEG Processing with MATLAB

Electroencephalography (EEG) is a widely used technique for measuring electrical activity in the brain, providing valuable insights into neural dynamics. Processing EEG data involves several steps, from raw signal acquisition to feature extraction and analysis. MATLAB serves as a powerful tool for handling EEG data due to its extensive Signal Processing Toolbox and flexibility in customizing workflows through scripting and function development.

Initial Stage: Data Loading and Preprocessing The first step in EEG processing involves loading raw data, typically stored in formats like .edf, .set, or .mat. MATLAB provides functions such as `edfread` for EDF files or EEGLAB's `pop_loadset` function (when using the EEGLAB toolbox) to import these files. For example: `eeg_data = edfread('filename.edf');` loads EDF data into a MATLAB structure. Once loaded, preprocessing becomes crucial for artifact and noise removal.

Common preprocessing steps include: Filtering – Applying bandpass filters (e.g., 0.5-50 Hz) using functions like `designfilt` or `pop_eegfiltnew` in EEGLAB to remove low-frequency drifts and high-frequency noise. Artifact Removal – Techniques like Independent Component Analysis (ICA) implemented through `pop_runica` can help isolate and eliminate artifacts such as eye blinks or muscle movements by decomposing signals into independent components. Segmentation – Epoching continuous signals into trials based on event markers using `pop_epoch` function, where triggers define trial boundaries. Baseline Correction – Adjusting each epoch by subtracting the mean signal from a pre-stimulus baseline period with functions like `pop_rmbase`.

Next Steps for Analysis After preprocessing, further analysis may include time-frequency decomposition using `spectrogram` or `wavelet` functions, event-related potential (ERP) analysis through averaging techniques, or machine learning-based classification with MATLAB's Classification Learner app. EEG-specific toolboxes like EEGLAB or FieldTrip offer built-in functions such as `pop_erpimage` for ERP visualization and `ft_freqanalysis` for frequency-domain analysis.

By implementing efficient preprocessing pipelines with proper MATLAB functions, researchers ensure cleaner data for downstream analysis, significantly improving the reliability of findings in neuroimaging studies through reproducible code-based workflows.