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Artifact (error)

About: Artifact (error) is a(n) research topic. Over the lifetime, 6212 publication(s) have been published within this topic receiving 119693 citation(s). The topic is also known as: artefact & artifacts. more


Journal ArticleDOI: 10.1016/0013-4694(83)90135-9
Abstract: A new off-line procedure for dealing with ocular artifacts in ERP recording is described. The procedure (EMCP) uses EOG and EEG records for individual trials in an experimental session to estimate a propagation factor which describes the relationship between the EOG and EEG traces. The propagation factor is computed after stimulus-linked variability in both traces has been removed. Different propagation factors are computed for blinks and eye movements. Tests are presented which demonstrate the validity and reliability of the procedure. ERPs derived from trials corrected by EMCP are more similar to a 'true' ERP than are ERPs derived from either uncorrected or randomly corrected trials. The procedure also reduces the difference between ERPs which are based on trials with different degrees of EOG variance. Furthermore, variability at each time point, across trials, is reduced following correction. The propagation factor decreases from frontal to parietal electrodes, and is larger for saccades than blinks. It is more consistent within experimental sessions than between sessions. The major advantage of the procedure is that it permits retention of all trials in an ERP experiment, irrespective of ocular artifact. Thus, studies of populations characterized by a high degree of artifact, and those requiring eye movements as part of the experimental task, are made possible. Furthermore, there is no need to require subjects to restrict eye movement activity. In comparison to procedures suggested by others, EMCP also has the advantage that separate correction factors are computed for blinks and movements and that these factors are based on data from the experimental session itself rather than from a separate calibration session. more

Topics: Artifact (error) (55%)

4,534 Citations

Journal ArticleDOI: 10.1111/1469-8986.3720163
Tzyy-Ping Jung1, Tzyy-Ping Jung2, Scott Makeig1, Colin Humphries2  +6 moreInstitutions (2)
01 Mar 2000-Psychophysiology
Abstract: Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity. more

2,681 Citations

Journal ArticleDOI: 10.1016/0004-3702(87)90063-4
J. de Kleer1, Brian C. Williams2Institutions (2)
Abstract: Diagnostic tasks require determining the difierences between a model of an artifact and the artifact itself. The difierences between the manifested behavior of the artifact and the predicted behavior of the model guide the search for the difierences between the artifact and its model. The diagnostic procedure presented in this paper is model-based, inferring the behavior of the composite device from knowledge of the structure and function of the individual components comprising the device. The system (GDE | General Diagnostic Engine) has been implemented and tested on many examples in the domain of troubleshooting digital circuits. This research makes several novel contributions: First, the system diagnoses failures due to multiple faults. Second, failure candidates are represented and manipulated in terms of minimal sets of violated assumptions, resulting in an e‐cient diagnostic procedure. Third, the diagnostic procedure is incremental, exploiting the iterative nature of diagnosis. Fourth, a clear separation is drawn between diagnosis and behavior prediction, resulting in a domain (and inference procedure) independent diagnostic procedure. Fifth, GDE combines modelbased prediction with sequential diagnosis to propose measurements to localize the faults. The normally required conditional probabilities are computed from the structure of the device and models of its components. This capability results from a novel way of incorporating probabilities and information theory into the context mechanism provided by AssumptionBased Truth Maintenance. more

Topics: Artifact (error) (52%)

2,164 Citations

Open accessBook
Donald A. Norman1Institutions (1)
20 Apr 1993-
Abstract: * A Human-Centered Technology * Experiencing the World * The Power of Representation * Fitting the Artifact to the Person * The Human Mind * Distributed Cognition * A Place for Everything, and Everything in Its Place * Predicting the Future * Soft and Hard Technology * Technology Is Not Neutral more

1,574 Citations

Open accessJournal ArticleDOI: 10.1016/J.NEUROIMAGE.2006.11.004
15 Feb 2007-NeuroImage
Abstract: Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, here tested using three popular ICA algorithms, Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data. We tested the upper bound performance of five methods for detecting various types of artifacts by separately optimizing and then applying them to artifact-free EEG data into which we had added simulated artifacts of several types, ranging in size from thirty times smaller (-50 dB) to the size of the EEG data themselves (0 dB). Of the methods tested, those involving spectral thresholding were most sensitive. Except for muscle artifact detection where we found no gain of using ICA, all methods proved more sensitive when applied to the ICA-decomposed data than applied to the raw scalp data: the mean performance for ICA was higher and situated at about two standard deviations away from the performance distribution obtained on raw data. We note that ICA decomposition also allows simple subtraction of artifacts accounted for by single independent components, and/or separate and direct examination of the decomposed non-artifact processes themselves. more

Topics: Independent component analysis (56%), FastICA (54%), Artifact (error) (54%) more

1,244 Citations

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Topic's top 5 most impactful authors

Saeid Sanei

10 papers, 262 citations

Sabine Van Huffel

7 papers, 129 citations

Prahlad K. Sethi

7 papers, 22 citations

Dante Mantini

6 papers, 342 citations

Andrzej Cichocki

6 papers, 89 citations