ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features

Mognon, Andrea; Jovicich, Jorge; Bruzzone, Lorenzo; Buiatti, M.

doi:10.1111/j.1469-8986.2010.01061.x

Cited by 1,132 publications

(838 citation statements)

References 35 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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“…Epochs were selected from À 200 ms to 1200 ms relative to the onset of the stimulus word. Ocular artefacts were automatically corrected with independent component analysis (ICA) in the ADJUST toolbox (Mognon et al, 2010). This procedure requires gross artefact correction before applying ICA correction, which resulted in the rejection of trials for each condition (Incongruent Go/Congruent Go/Incongruent NoGo/Congruent NoGo) with mean (SD) as follows: 1.84(1.99), 1.81(2.04), 1.92(2.13), and 1.76(1.64) for joint action, and 4.…”

Section: Eeg Signal Processingmentioning

confidence: 99%

Neural correlates of verbal joint action: ERPs reveal common perception and action systems in a shared-Stroop task

et al. 2016

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a b s t r a c tRecent social-cognitive research suggests that the anticipation of co-actors' actions influences people's mental representations. However, the precise nature of such representations is still unclear. In this study we investigated verbal joint representations in a delayed Stroop paradigm, where each participant responded to one color after a short delay. Participants either performed the task as a single actor (singleaction, Experiment 1), or they performed it together (joint-action, Experiment 2). We investigated effects of co-actors' actions on the ERP components associated with perceptual conflict (Go N2) and response selection (P3b). Compared to single-action, joint-action reduced the N2 amplitude congruency effect when participants had to respond (Go trials), indicating that representing a co-actor's utterance helped to dissociate action codes and attenuated perceptual conflict for the responding participant. Yet, on NoGo trials the centro-parietal P3 (P3b) component amplitude increased for joint-action, suggesting that participants mapped the stimuli onto the co-actor's upcoming response as if it were their own response. We conclude that people represent others' utterances similarly to the way they represent their own utterances, and that shared perception-action codes for self and others can sometimes reduce, rather than enhance, perceptual conflict.

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“…To further minimize artifacts, the ADJUST toolbox was used. It operates upon implementation of independent component analysis (ICA) to automatically detect artifact independent components based on the artifact-specific spatial and temporal features (Mognon et al, 2011). The data were then re-referenced to an average reference.…”

Section: Eeg Recordings and Data Analysesmentioning

confidence: 99%

Contingent negative variation (CNV) associated with sensorimotor timing error correction

et al. 2016

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Introduction: Detection and subsequent correction of sensorimotor timing errors is fundamental to adaptive behavior. Using scalp-recorded event-related potentials (ERPs), we sought to find ERP components that are predictive of error correction performance during rhythmic movements.Method: Healthy right-handed participants were asked to synchronize their finger taps to a regular tone sequence (every 600 ms), whilst EEG data were continuously recorded. Data from 15 participants were analyzed. Occasional irregularities were built into stimulus presentation timing: 90 ms before (advances: negative shift) or after (delays: positive shift) the expected time point. A tapping condition alternated with a listening condition in which identical stimulus sequence was presented but participants did not tap.Results: Behavioral error correction was observed immediately following a shift, with a degree of over-correction with positive shifts. Our stimulus-locked ERP data analysis revealed, 1) increased auditory N1 amplitude for the positive shift condition and decreased auditory N1 modulation for the negative shift condition; and 2) a second enhanced negativity (N2) in the tapping positive condition, compared with the tapping negative condition. In response-locked epochs, we observed a CNV (contingent negative variation)-like negativity with earlier latency in the tapping negative condition compared with the tapping positive condition. This CNV-like negativity peaked at around the onset of subsequent tapping, with the earlier the peak, the better the error correction performance with the negative shifts while the later the peak, the better the error correction performance with the positive shifts.Discussion: This study showed that the CNV-like negativity was associated with the error correction performance during our sensorimotor synchronization study. Auditory N1 and N2 were differentially involved in negative vs. positive error correction. However, we did not find 3 evidence for their involvement in behavioral error correction. Overall, our study provides the basis from which further research on the role of the CNV in perceptual and motor timing can be developed.

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ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features

Cited by 1,132 publications

References 35 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

Neural correlates of verbal joint action: ERPs reveal common perception and action systems in a shared-Stroop task

Contingent negative variation (CNV) associated with sensorimotor timing error correction

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