Automatic removal of EEG artifacts using electrode-scalp impedance

Due to the low signal-to-noise ratio of electroencephalographic (EEG) recordings, the quality of the electrode-scalp contact is an important factor in EEG-based brain-computer interfaces (BCIs). For this reason, the impedance between each individual electrode and the scalp is measured prior to each EEG recording session. In order to obtain high quality EEG signals and accurate performance, the impedance has to be low (below 5K Ohms). Typically, researchers have reduced the electrode-scalp impedance by performing time-consuming electrode adjustments prior to the data acquisition stage. In this paper, we utilize the electrode-scalp impedance information to remove the EEG artifacts caused by high impedance electrodes in order to enhance the signal quality during the signal processing stage. Our proposed method is based on the independent component analysis (ICA) algorithm, which is used to decompose the EEG signals into independent components. The electrode-scalp impedance is employed to automatically distinguish irrelevant components from event-related components. The experimental results show that our method can effectively remove artifacts and enhance the BCI performance compared to the scenario where no artifacts were removed, and the scenario in which irrelevant independent components were removed manually based on prior knowledge.