Real-time motion artifact removal using a dual-stage median filter. (February 2022)

• Record Type: Journal Article
• Authors: Huang, Ruisen; Qing, Kunqiang; Yang, Dalin; Hong, Keum-Shik
• Is Part Of: Biomedical signal processing and control. Volume 72(2022)Part A
• Publication Date: 2022-02
• Subjects: Dual-stage median filter -- Functional near-infrared spectroscopy -- Motion artifacts -- Low-frequency drift -- Balloon model; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103301
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A dual-stage median filter (DSMF) is proposed to remove motion artifacts in the fNIRS signals. The proposed filter removes the low-frequency drifts. DSMF outperforms existing motion artifacts removal methods in signal distortion and noise suppression. DSMF can be implemented for real-time applications. Abstract: Functional near-infrared spectroscopy (fNIRS) is an emerging technique for the non-invasive brain-computer interface (BCI). Quickly obtaining precise brain signals is very crucial for successful BCIs. This paper investigates a real-time filtering technique to remove motion artifact (MA) and low-frequency drift in the fNIRS signals. Optical intensities of two wavelengths are generated using a balloon model in the literature and an experimental paradigm. Two types of MAs (spike-like and step-like) and low-frequency drifts are generated and added to the simulated optical intensities of two wavelengths. A new dual-stage median filter (DSMF) is proposed to recover the uncontaminated signals. Five evaluation metrics are used to determine the best window sizes of the dual filters: 4 s and 9 s for the first and 18 s for the second median filter. The proposed method is compared with a wavelet-based MA correction method and spline interpolation method using the same metrics. The results show that the proposed method outperforms the compared methods in attenuating MAs and signal distortion. Finally, the designed DSMF is applied to experimental data from eight healthy subjects, in which MAs were introduced by asking the subjects to shake their heads. The filtered data of the proposed method demonstrates clean signals with no MAs and low-frequency drifts.