A Hybrid Framework for ERP Preprocessing in EEG Experiments

Methods to derive information on neural processes from Electroencephalographic (EEG) signals become increasingly complex, especially with the introduction of deep learning algorithms. However, considering the low Signal-to-Noise Ratio (SNR) of raw EEG signals, the input data should be properly preprocessed. Common preprocessing algorithms using single method struggle to reduce several types of artifacts/noises without affecting the useful parts of the signal. We therefore propose a hybrid preprocessing framework to combine strengths of multiple state-of-the-art approaches. The latter method provides output signals of better quality than a common Independent Component Analysis (ICA) based pipeline on simulated data. Our pipeline provides a standard to clean a wide range of Event-Related Potential (ERP) signals as required to improve the accuracy of further inference on neural processes.