Imaging human EEG dynamics using independent component analysis

Onton, Julie; Westerfield, Marissa; Townsend, Jeanne; Makeig, Scott

doi:10.1016/j.neubiorev.2006.06.007

Cited by 605 publications

(484 citation statements)

References 41 publications

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“…We see a major utility for parallel ICA in this context as it provides the means to disentangle and visualize these networks both in their spatial and temporal form (Calhoun, et al, 2006a;Debener, et al, 2006;Makeig, et al, 2004a;McKeown, et al, 2003;Onton, et al, 2006). However, some limitations apply: Infomax assumes sources to have non-normal, either superor subgaussian distributions (Bell, et al, 1995;Lee, et al, 1999), and this seems to hold for a great variety of physiological signals as well as technical artefacts.…”

Section: Area Of Applicationmentioning

confidence: 99%

“…The reason for this is that a salient event can induce multiple, simultaneously active, regionally overlapping, and functionally separable responses which add to existing neuronal background activity, in other words, event-related processes are spatially and temporally mixed across the brain. The scalp EEG samples a volume-conducted, spatially degraded version of the responses, where the potential at any location and latency can be considered a mixture of multiple independent timecourses that stem from large-scale synchronous field potentials (Makeig, et al, 2004a;Onton, et al, 2006). Similarly, the neurovascular transformation of the distributed neuronal activity into hemodynamic signals (Lauritzen, et al, 2003;Logothetis, 2003) affords detection of blood oxygenation level dependent responses (BOLD, Ogawa, et al, 1990) that are temporally degraded and spatially mixed across the fMRI volume (Calhoun, et al, 2006a;McKeown, et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm used here attempts to separate mixed signals into maximally independent sources by maximization of information transfer between them. ICA has general applicability to normally distributed two-dimensional mixtures, and regarding psychophysiological data it has been used for decomposition of averaged ERPs (Makeig, et al, 1997), single trial EEG (Makeig, et al, 2004b;Onton, et al, 2006), fMRI (Calhoun, et al, 2006a) and EEG-fMRI (Calhoun, et al, 2006c;Debener, et al, 2005b;Feige, et al, 2005). ICA can be used for EEG-fMRI integration assuming that the different recording modalities faithfully sample features from the same set of sources, expressed in the covariation between single trials (Debener, et al, 2005b) or subjects (Calhoun, et al, 2006c).…”

Section: Introductionmentioning

confidence: 99%

“…There are two strategies to allow for matching of independent components across individuals: one is to combine individual ICs across subjects with clustering techniques Onton, et al, 2006). Another approach is to create aggregate data containing observations from all subjects, estimate a single set of ICs and then back-reconstruct these in the individual data (Calhoun, et al, 2001;Schmithorst, et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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Unmixing concurrent EEG-fMRI with parallel independent component analysis

Eichele

Calhoun

Moosmann

et al. 2008

International Journal of Psychophysiology

104

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Concurrent event-related EEG-fMRI recordings pick up volume-conducted and hemodynamically convoluted signals from latent neural sources that are spatially and temporally mixed across the brain, i.e. the observed data in both modalities represent multiple, simultaneously active, regionally overlapping neuronal mass responses. This mixing process decreases the sensitivity of voxel-byvoxel prediction of hemodynamic activation by the EEG when multiple sources contribute to either the predictor and/or the response variables. In order to address this problem, we used independent component analysis (ICA) to recover maps from the fMRI and timecourses from the EEG, and matched these components across the modalities by correlating their trial-to-trial modulation. The analysis was implemented as a group-level ICA that extracts a single set of components from the data and directly allows for population inferences about consistently expressed function-relevant spatiotemporal responses. We illustrate the utility of this method by extracting a previously undetected but relevant EEG-fMRI component from a concurrent auditory target detection experiment. §Corresponding Author:

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Section: Area Of Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unmixing concurrent EEG-fMRI with parallel independent component analysis

Eichele

Calhoun

Moosmann

et al. 2008

International Journal of Psychophysiology

104

View full text Add to dashboard Cite

show abstract

“…The nonlinear analysis method used here, recurrence analysis (22), encompasses other quantifiers in addition to %R and %D, and other nonlinear processing methods have been described (27)(28)(29). The relative sensitivity and utility of other quantifiers and processing approaches have not been determined.…”

Section: Discussionmentioning

confidence: 99%

Simulated MR magnetic field induces steady‐state changes in brain dynamics: Implications for interpretation of functional MR studies

Marino

Carrubba

Frilot

et al. 2010

Magnetic Resonance in Med

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We examined whether a magnetic field comparable to one of the fields produced during MRI induced steady-state changes in brain electrical activity while the field was applied (called a presence effect to distinguish it from evoked potentials). The electroencephalogram was measured from standard scalp locations in the presence and absence of 100-200 mT, 60 Hz, and the effect of the field was evaluated by nonlinear (recurrence analysis) and linear techniques; individual subjects served as their own controls. Using recurrence analysis, changes in brain activity lasting 1 sec (the longest interval considered) were found in 21 of 22 subjects (P < 0.05 for each subject). The presence effect was not detected using linear analysis and was reversible, as indicated by a return of brain activity to baseline levels in all subjects within 2 sec of field offset. The possible role of artifacts or systematic errors was ruled out by studies using electrical phantoms and by analyses of electroencephalograms recorded during sham exposure. It is reasonable to expect that actual scanner magnetic fields also produce nonlinear steady-state perturbations of brain dynamical activity. The effect may influence the picture of brain connectivity inferred in some functional MR studies. Magn Reson Med 64:349-357, 2010. V C 2010 Wiley-Liss, Inc.Key words: nonlinear analysis; brain electrical activity; limitation on fMRI; electric fields; magnetic fields Cognitive activity is mediated by temporal-spatial interactions involving specialized regions of the brain (1). The interactions are commonly studied using functional MRI (fMRI) employing blood-oxygen-level-dependent (BOLD) contrast imaging (2).Averaged images obtained when subjects were and were not performing particular tasks are typically used to identify brain regions that mediated task-related cognitive processing (3,4). This strategy is based on some problematical assumptions and has neuropsychological drawbacks (5), but it affords good statistical power for detecting BOLD signals (6).In MRI, static (0 Hz) magnetic fields of 1-9 T align nuclear spins, high-frequency magnetic fields (10-400 MHz) of about 0.2 mT induce transitions between spin states, and low-frequency magnetic fields (100-1000 Hz) of about 50 mT produce imaging gradients (7); in fMRI, gradient parameters produce additional magnetic fields. Physical law and physiologic conditions in the brain (blood flow, tissue motion, cell movement, motion of the head) result in the simultaneous presence of electric fields (8).Human subjects can detect magnetic fields, as evidenced by their ability to trigger onset and offset evoked potentials (9). We were interested in whether, in addition to these transient changes in brain electrical activity, magnetic fields relevant to those used in MRI also produced continuous changes during the time the field was applied (''presence effect''). Depending on the dynamical nature of the effect (see below), such a finding would enhance the possibility that BOLD data obtained during functional imaging c...

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Detecting Glaucoma With a Portable Brain-Computer Interface for Objective Assessment of Visual Function Loss

et al. 2017

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The current assessment of visual field loss in diseases such as glaucoma is affected by the subjectivity of patient responses and the lack of portability of standard perimeters.OBJECTIVE To describe the development and initial validation of a portable brain-computer interface (BCI) for objectively assessing visual function loss. DESIGN, SETTING, AND PARTICIPANTSThis case-control study involved 62 eyes of 33 patients with glaucoma and 30 eyes of 17 healthy participants. Glaucoma was diagnosed based on a masked grading of optic disc stereophotographs. All participants underwent testing with a BCI device and standard automated perimetry (SAP) within 3 months. The BCI device integrates wearable, wireless, dry electroencephalogram and electrooculogram systems and a cellphone-based head-mounted display to enable the detection of multifocal steady state visual-evoked potentials associated with visual field stimulation. The performances of global and sectoral multifocal steady state visual-evoked potentials metrics to discriminate glaucomatous from healthy eyes were compared with global and sectoral SAP parameters. The repeatability of the BCI device measurements was assessed by collecting results of repeated testing in 20 eyes of 10 participants with glaucoma for 3 sessions of measurements separated by weekly intervals. MAIN OUTCOMES AND MEASURESReceiver operating characteristic curves summarizing diagnostic accuracy. Intraclass correlation coefficients and coefficients of variation for assessing repeatability. RESULTS Among the 33 participants with glaucoma, 19 (58%) were white, 12 (36%) were black, and 2 (6%) were Asian, while among the 17 participants with healthy eyes, 9 (53%) were white, 8 (47%) were black, and none were Asian. The receiver operating characteristic curve area for the global BCI multifocal steady state visual-evoked potentials parameter was 0.92 (95% CI, 0.86-0.96), which was larger than for SAP mean deviation (area under the curve, 0.81; 95% CI, 0.72-0.90), SAP mean sensitivity (area under the curve, 0.80; 95% CI, 0.69-0.88; P = .03), and SAP pattern standard deviation (area under the curve, 0.77; 95% CI, 0.66-0.87; P = .01). No statistically significant differences were seen for the sectoral measurements between the BCI and SAP. Intraclass coefficients for global and sectoral parameters ranged from 0.74 to 0.92, and mean coefficients of variation ranged from 3.03% to 7.45%. CONCLUSIONS AND RELEVANCEThe BCI device may be useful for assessing the electrical brain responses associated with visual field stimulation. The device discriminated eyes with glaucomatous neuropathy from healthy eyes in a clinically based setting. Further studies should investigate the feasibility of the BCI device for home-based testing as well as for detecting visual function loss over time.

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Imaging human EEG dynamics using independent component analysis

Cited by 605 publications

References 41 publications

Unmixing concurrent EEG-fMRI with parallel independent component analysis

Unmixing concurrent EEG-fMRI with parallel independent component analysis

Simulated MR magnetic field induces steady‐state changes in brain dynamics: Implications for interpretation of functional MR studies

Detecting Glaucoma With a Portable Brain-Computer Interface for Objective Assessment of Visual Function Loss

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