Removal of Movement Artefact for Mobile EEG Analysis in Sports Exercises

Abstract

We present a method for the removal of movement artifacts from the recordings of electroencephalography (EEG) signals in the context of sports health. We use a smart wearable Internet of Things-based signal recording system to record physiological human signals [EEG, electrocardiography (ECG)] in real time. Then, the movement artifacts are removed using ECG as a reference signal and the baseline estimation and denoising with sparsity (BEADS) filter algorithm for trend removal. The parameters (cut-off frequency) of the BEADS filter are optimized with respect to the number of QRS complexes detected in the reference ECG signal. Next, surrogate movement signals are generated using a linear combination of intrinsic mode functions derived from the sample movement signals by the application of empirical mode decomposition. Surrogate signals are used to test the efficiency of the BEADS method for filtering the movement-contaminated EEG signals. We provide an analysis of the efficiency of the method, extracted movement artifacts and detrended EEG signals.