On-line computer rejection of EEG artifact

Gevins, Alan; Yeager, Charles L.; Zeitlin, G.M; Ancoli, Sonia; Dedon, M.F

doi:10.1016/0013-4694(77)90035-9

Cited by 64 publications

(20 citation statements)

References 5 publications

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“…Rowland (1968) incorporated a similar thresholding method by using additional physiological signals (EOG and EMG), and rejected any EEG sections if the corresponding EOG or EMG signal exceeded a pre-determined threshold. Gevins et al (1977) used thresholding of the EEG in different frequency bands to reduce ocular artefacts. A similar method was employed by Pfurtscheller et al (1996) and McFarland et al (1997) to reduce EMG artefact contamination in the EEG for BCI applications.…”

Section: Thresholding Methodsmentioning

confidence: 99%

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

O’Regan¹

2018

Preprint

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Type of publicationDoctoral thesis Rights AbstractThe electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of-use and mobility are important.Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours.However, as typical EEG signals are of the order of microvolts (µV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance.This thesis therefore, contributes to the further improvement of automated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science.The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning i classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG.Subsequently, additional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head-movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement artefact detection.The artefact detecti...

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Section: Thresholding Methodsmentioning

confidence: 99%

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

O’Regan¹

2018

Preprint

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“…Ktonas et al [37], for instance, defined departures from a normal amplitude distribution, evaluated by a ¯2 test, as artifacts in sleep EEG data. Already in 1977, Gevins et al [38] described an artifact identification method based on departures from features calibrated from short visually evaluated artifact-free data segments. John et al [39] extended this approach by using adaptive thresholds based on moving averages.…”

Section: Artifact Identificationmentioning

confidence: 99%

Artifact Processing in Computerized Analysis of Sleep EEG – A Review

Anderer¹,

Roberts²,

Schlögl³

et al. 1999

Neuropsychobiology

141

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Quantitative analysis of sleep EEG data can provide valuable additional information in sleep research. However, analysis of data contaminated by artifacts can lead to spurious results. Thus, the first step in realizing an automatic sleep analysis system is the implementation of a reliable and valid artifact processing strategy. This strategy should include: (1) high-quality recording techniques in order to minimize the occurrence of avoidable artifacts (e.g. technical artifacts); (2) artifact minimization procedures in order to minimize the loss of data by estimating the contribution of different artifacts in the EEG recordings, thus allowing the calculation of the ‘corrected’ EEG (e.g. ocular and ECG interference), and finally (3) artifact identification procedures in order to define epochs contaminated by remaining artifacts (e.g. movement and muscle artifacts). Therefore, after a short description of the types of artifacts in the sleep EEG and some typical examples obtained in different sleep stages, artifact minimization and identification procedures will be reviewed.

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“…Even if the techniques are not yet perfect and details remain to be worked out, it is our opinion that their level is sufficiently developed and the time is ripe to justify the introduction of robust methods into the field of EEG where, to our knowledge, little or no use of them has been made so far. This does not mean that EEGers have not been aware of the problems caused by artifacts and other transients in connection with spectral analysis of the EEG: the contrary is true and the literature abunds of ad hoc schemes for artifacts detection and transient recogni tion [e.g., Gevins et al, 1977, or Barlow, 1985.…”

Section: Robustness In Time Series and The Eegmentioning

confidence: 99%

Robust Spectral Analysis of the EEG

Molinari¹,

Dumermuth²

1986

Neuropsychobiology

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Robust methods for the spectral analysis of time series are briefly reviewed and seen to have applications in the field of EEG. After presenting two simple schemes for outliers (artifacts) generation and discussing their implications for estimation of the spectral density, the robust filtering algorithm of Kleiner et al. [J. R. Statist. Soc. Ser. B, 41: 313-351, 1979] is introduced and shown to work well for simulated data and for true EEG data containing artifacts. A new use of the robust methods for the detection of artifacts and possibly other transients in long-time EEG recordings is suggested and a preliminary implementation illustrated.

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On-line computer rejection of EEG artifact

Cited by 64 publications

References 5 publications

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

Artifact Processing in Computerized Analysis of Sleep EEG – A Review

Robust Spectral Analysis of the EEG

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