Artifact removal in EEG using Morphological Component Analysis

To reduce the effects of artifacts in electroencephalography (EEG), we propose the use of Morphological Component Analysis (MCA). Taking advantage of the sparse representation of data in overcomplete dictionaries, MCA decomposes EEG signals into parts that have different morphological characteristics. For denoising purpose, the parts related to artifacts are removed. An overcomplete dictionary is constructed using the discrete cosine transform, Daubechies wavelet basis, and Dirac basis. Movement-related potentials (MRP) and EEG signals contaminated by spikes, eye-blinks, and muscle artifacts caused by eye-brow raising are used to evaluate the performance of the method. The results demonstrate that MCA can be used to decompose the single-channel EEG signals into artifacts and MRP components. The correlation coefficient between the denoised MRP and the original MRP using MCA is significantly higher than that obtained using stationary wavelet transform.