Reconstruction and Denoising of EEG Signal Using Alternating Direction Method of Multipliers

The tremendous progress of big data acquisition and processing in the field of neural engineering has enabled a better understanding of the patient’s brain disorders with their neural rehabilitation, restoration, detection, and diagnosis. Electroencephalography (EEG) is a method to record an electrogram of the electrical activity on the scalp that has been shown to represent the macroscopic activity of the surface layer of the brain underneath. However, EEG signals are contained by different noises. This report presents efficient reconstruction and denoising of EEG signal based on Alternating Direction Method of Multipliers. The alternating direction method of multipliers (ADMM) has been widely explored due to its broad applications, and its convergence has been gotten in the real field. This report discusses the alternating direction method of multipliers (ADMM), a simple but powerful algorithm that is well suited to distributed convex optimization, and in particular to problems arising in applied statistics and machine learning. It takes the form of a decomposition-coordination procedure, in which the solutions to small local subproblems are coordinated to find a solution to a large global problem. The algorithm in the report show that the proposed ADMM-based method performs better in EEG-denoising than Compress Sensing (CS) method which is simple and traditional method. Furthermore, the proposed algorithm keeps the detail of the EEG-Signal in reconstruction and achieves smaller root means square error (RMSE) for small-iteration.