Motion artifact removal techniques, including electrode design, analog circuits, and signal processing algorithms, are essential for mitigating signal distortion in wearable EEG and PPG sensor systems.
Removal of motion artifacts is a critical challenge, especially in wearable electroencephalography (EEG) and photoplethysmography (PPG) devices that are exposed to daily movements. Recently, the significance of motion artifact removal techniques has increased since EEG-based brain–computer interfaces (BCI) and daily healthcare usage of wearable PPG devices were spotlighted. In this article, the development on EEG and PPG sensor systems is introduced. Then, understanding of motion artifact and its reduction methods implemented by hardware and/or software fashions are reviewed. Various electrode types, analog readout circuits, and signal processing techniques are studied for EEG motion artifact removal. In addition, recent in-ear EEG techniques with motion artifact reduction are also introduced. Furthermore, techniques compensating independent/dependent motion artifacts are presented for PPG.
Seok et al. (Thu,) conducted a review in Motion artifacts in wearable EEG and PPG devices. Motion artifact removal techniques was evaluated. Motion artifact removal techniques, including electrode design, analog circuits, and signal processing algorithms, are essential for mitigating signal distortion in wearable EEG and PPG sensor systems.