Removal of Movement Artefact for Mobile EEG Analysis in Sports Exercises

Removal of Movement Artefact for Mobile EEG Analysis in Sports Exercises

Blog Article

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 Hubcap 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 Tanks/Jerseys 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.