A Real-Time Movement Artifact Removal Method for Ambulatory Brain-Computer Interfaces

Young Eun Lee, No Sang Kwak, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


Recently, practical brain-computer interfaces (BCIs) have been widely investigated for detecting human intentions in real world. However, performance differences still exist between the laboratory and the real world environments. One of the main reasons for such differences comes from the user's unstable physical states (e.g., human movements are not strictly controlled), which produce unexpected signal artifacts. Hence, to minimize the performance degradation of electroencephalography (EEG)-based BCIs, we present a novel artifact removal method named constrained independent component analysis with online learning (cIOL). The cIOL can find and reject the noise-like components related to human body movements (i.e., movement artifacts) in the EEG signals. To obtain movement information, isolated electrodes are used to block electrical signals from the brain using high-resistance materials. We estimate artifacts with movement information using constrained independent component analysis from EEG signals and then extract artifact-free signals using online learning in each sample. In addition, the cIOL is evaluated by signal processing under 16 different experimental conditions (two types of EEG devices times two BCI paradigms times four different walking speeds). The experimental results show that the cIOL has the highest accuracy in both scalp- and ear-EEG, and has the highest signal-to-noise ratio in scalp-EEG among the state-of-the-art methods, except for the case of steady-state visual evoked potential at 2.0 m/s with superposition problem.

Original languageEnglish
Article number9269008
Pages (from-to)2660-2670
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number12
Publication statusPublished - 2020 Dec

Bibliographical note

Funding Information:
Manuscript received June 16, 2020; revised October 9, 2020; accepted November 11, 2020. Date of publication November 24, 2020; date of current version January 29, 2021. This work was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea Government (MSIT) (Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions Using Deep Learning) under Grant 2017-0-00451, (Development of Intelligent Pattern Recognition Softwares for Ambulatory Brain Computer Interface) under Grant 2015-0-00185, and (Artificial Intelligence Graduate School Program, Korea University) under Grant 2019-0-00079. (Corresponding author: Seong-Whan Lee.) Young-Eun Lee is with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea (e-mail: ye_lee@korea.ac.kr).

Publisher Copyright:
© 2001-2011 IEEE.


  • Electroencephalography (EEG)
  • ambulatory environment
  • artifact removal
  • constrained independent component analysis (cICA)
  • online learning

ASJC Scopus subject areas

  • Internal Medicine
  • General Neuroscience
  • Biomedical Engineering
  • Rehabilitation


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