FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection

Nolan, Hugh; Whelan, Robert; Reilly, Richard B.

doi:10.1016/j.jneumeth.2010.07.015

Cited by 935 publications

(738 citation statements)

References 17 publications

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“…However, a current limitation of the EEG-based assessments proposed here stems from expert intervention required for artefact removal, specifically for inspecting and identifying noisy data and independent components. There have been many recent methodological advances in automating this step (Nolan et al, 2010;Mognon et al, 2011;Jas et al, 2016), and future work towards validating these methods with patient datasets could help develop the analytical pipeline for clinical applications.…”

Section: Discussionmentioning

confidence: 99%

Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Chennu

Annen²,

Wannez³

et al. 2017

Brain

250

255

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Recent advances in functional neuroimaging have demonstrated novel potential for informing diagnosis and prognosis in the unresponsive wakeful syndrome and minimally conscious states. However, these technologies come with considerable expense and difficulty, limiting the possibility of wider clinical application in patients. Here, we show that high density electroencephalography, collected from 104 patients measured at rest, can provide valuable information about brain connectivity that correlates with behaviour and functional neuroimaging. Using graph theory, we visualize and quantify spectral connectivity estimated from electroencephalography as a dense brain network. Our findings demonstrate that key quantitative metrics of these networks correlate with the continuum of behavioural recovery in patients, ranging from those diagnosed as unresponsive, through those who have emerged from minimally conscious, to the fully conscious locked-in syndrome. In particular, a network metric indexing the presence of densely interconnected central hubs of connectivity discriminated behavioural consciousness with accuracy comparable to that achieved by expert assessment with positron emission tomography. We also show that this metric correlates strongly with brain metabolism. Further, with classification analysis, we predict the behavioural diagnosis, brain metabolism and 1-year clinical outcome of individual patients. Finally, we demonstrate that assessments of brain networks show robust connectivity in patients diagnosed as unresponsive by clinical consensus, but later rediagnosed as minimally conscious with the Coma Recovery Scale-Revised. Classification analysis of their brain network identified each of these misdiagnosed patients as minimally conscious, corroborating their behavioural diagnoses. If deployed at the bedside in the clinical context, such network measurements could complement systematic behavioural assessment and help reduce the high misdiagnosis rate reported in these patients. These metrics could also identify patients in whom further assessment is warranted using neuroimaging or conventional clinical evaluation. Finally, by providing objective characterization of states of consciousness, repeated assessments of network metrics could help track individual patients longitudinally, and also assess their neural responses to therapeutic and pharmacological interventions. Keywords: disorders of consciousness; electroencephalography; positron emission tomography; resting state; brain networks Abbreviations: AUC = area under the curve; CRS-R = Coma Recovery Scale-Revised; dwPLI = debiased weighted phase lag index;GOS-E = Glasgow Outcome Scale-Extended; MCS = minimally conscious state; SVM = support vector machine; UWS = unresponsive wakefulness syndrome

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Section: Discussionmentioning

confidence: 99%

Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Chennu

Annen²,

Wannez³

et al. 2017

Brain

250

255

View full text Add to dashboard Cite

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“…The EEG data were segmented into epochs beginning 2250 ms before and lasting 1250 ms after the potential onset of a stimulus. After epoching, the Fully Automated Statistical Thresholding for EEG Artefact Rejection plugin for EEGLAB (Nolan, Whelan, & Reilly, 2010) was used for general artefact rejection and interpolation of globally and locally artefact contaminated channels, supplemented by visual inspection for further periods of non-standard data, such as voltage jumps, blinks, and muscle noise.…”

Section: Eeg Recording and Processingmentioning

confidence: 99%

Pre-stimulus alpha oscillations over somatosensory cortex predict tactile misperceptions

et al. 2017

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Cite this article as: Matt Craddock, Ellen Poliakoff, Wael El-deredy, Ekaterini Klepousniotou and Donna M. Lloyd, Pre-stimulus alpha oscillations over somatosensory cortex predict tactile misperceptions, Neuropsychologia, http://dx.doi.org/10. 1016/j.neuropsychologia.2016.12.030 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractFluctuations of pre-stimulus oscillatory activity in the somatosensory alpha band (8-14 Hz) observed using human EEG and MEG have been shown to influence the detection of supraand peri-threshold somatosensory stimuli. However, some reports of touch occur even without a stimulus. We investigated the possibility that pre-stimulus alpha oscillations might also influence these false reports of touch -known as tactile misperceptions. We recorded EEG while participants performed the Somatic Signal Detection Task (SSDT), in which participants must detect brief, peri-threshold somatosensory targets. We found that prestimulus oscillatory power in the somatosensory alpha range exhibited a negative linear 2 relationship with reporting of touch at electrode clusters over both contralateral and ipsilateral somatosensory regions. As pre-stimulus alpha power increased, the probability of reporting a touch declined; as it decreased, the probability of reporting a touch increased. This relationship was stronger on trials without a somatosensory stimulus than on trials with a somatosensory stimulus, although was present for both trial types. Spatio-temporal clusterbased permutation analysis also found that pre-stimulus alpha was lower on trials when touch was reported -irrespective of whether it was present -over contralateral and ipsilateral somatosensory cortices, as well as left frontocentral areas. We argue that alpha power may reflect changes in response criterion rather than sensitivity alone. Low alpha power relates to a low barrier to reporting a touch even when one is not present, while high alpha power is linked to less frequent reporting of touch overall.

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“…Artifact Correction Using Independent Component Analysis 1. Import the data into the FASTER toolbox 28 and run the automatic artifact rejection algorithm on the data ( Table 1).…”

Section: Discussionmentioning

confidence: 99%

Cortical Source Analysis of High-Density EEG Recordings in Children

Bathelt

O’Reilly

Haan

2014

JoVE

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EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint 1 . This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited 2 , because the composition and spatial configuration of head tissues changes dramatically over development 3 .In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis. Video LinkThe video component of this article can be found at

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FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection

Cited by 935 publications

References 17 publications

Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Pre-stimulus alpha oscillations over somatosensory cortex predict tactile misperceptions

Cortical Source Analysis of High-Density EEG Recordings in Children

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