EMG and EOG artifacts in brain computer interface systems: A survey.

By Mehrdad Fatourechi, Ali Bashashati, Rabab Kreidieh Ward, Gary E. Birch. Clinical Neurophysiology, 2007

670 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1a35fbbcff7e0843010fbc0f13f6ee4b0e56dd04

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In Cellular and Molecular Neuroscience

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Citations in the corpus

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “Notwithstanding these successes, it has been noted that many publications on EEG-based BCIs do not contain information on how EEG artifacts were handled (244).”
Riemannian Approaches in Brain-Computer Interfaces: A Review (2017, neutral): “2627016 user motions [9], as well as to the non-stationarity of EEG signals [10].”
Brain Computer Interfaces, a Review (2012, methodology): “Finally, PCA computes the feature vector v from the data in matrix A, by projecting the test data q onto the new subspace, such that: (2) where, ∑ represents the mean vector of training samples pi.”
EEG artifact removal—state-of-the-art and guidelines (2015, background): “be recorded [8]: changes in brain rhythms, movement-related”
Review of the BCI Competition IV (2012, methodology): “The EOG channels are provided for the subsequent application of artifact processing methods (Fatourechi et al., 2007) and must not be used for classification.”
EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy (2019, neutral): “…physiological signals such as EEG data for eye open/close, resting states, artifacts (e.g., head movement, eye blinking), and electromyography (EMG) data from both arms that could be suitable for machine learning and signal processing studies (e.g., optimization, artifact filtering) [28,29].”
BCI Competition 2008 { Graz data set B (2008, methodology): “The EOG channels are provided for the subsequent application of artifact processing methods [1] and must not be used for classification.”
Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces (2015, methodology): “…in small sample size settings, as when little data is available, a single outlier (artefacts being un-fortunately rather common in EEG measurements [82]) can have dramatic negative effects on the quality of the optimized BCI; 3) combining user-independent BCI de-sign followed by online adaptation…”
Removal of Artifacts from EEG Signals: A Review (2019, background): “For instance, eye movements, eye blinks, cardiac activity and muscle activity occurred in EEG signal are some major types of physiological artifacts [8].”
Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals (2011, methodology): “For an extensive review of artifact reduction techniques in the context of BCI-systems, the reader can refer to Fatourechi et al. [5].”
Brain-Computer Interfaces for Communication and Control (2011, neutral): “Operation of such a system is based on the as-yet uncertain premise that users can As noted, EEG recording may be contaminated by non-brain activity (such as line noise and muscle activity); see Fatourechi et al. 8 for a review.”
Robust artifactual independent component classification for BCI practitioners (2014, methodology): “For an extensive review of artifact reduction techniques in the context of BCI-systems, we refer the reader to [14].”
Reliable but Wrong? Automated Detection of Heart Rate Artifacts as a Source of Bias in Pediatric EEG Data (2026, unknown): “However, it is common for these methods to result in a drastic loss of usable data [7].”