Electroencephalogram signal processing with Python
Mahdi Taghaddossi, P Moradi, Mohammad Hassan Moradi · 2024
Analyzing electroencephalogram (EEG) signals is essential to aid the objective of many studies. As the registered EEG signals are contaminated with various noises, data preprocessing methods are employed to clean the signals. In this chapter, will discuss some of the methods and tools in Python to clean and process the EEG data. We will use Python libraries MNE, NumPy, Matplotlib, and Pandas to preprocess and make data usable for further machine learning algorithms and models. In detail, we exposed raw EEG signals in the time and frequency domain at the first stage. For the preprocessing stage, we filtered and resampled data, repaired artifacts of the signals by independent component analysis (ICA), handled the bad channels, rejected noisy data spans visually, and reset the EEG reference. We also segmented continuous data into epochs and estimated the evoke response, which is important in evoke-related potential (ERP) studies. Time frequency analysis and source localization modeling were performed in the processing stage as well. As a result, we showed the power of the Python language and MNE package in EEG signal analysis and illustrated their proficiency in EEG signal preprocessing and processing stages with informative and fascinating images.