Adaptive tracking of EEG oscillations

Zaen, Jérôme Van; Uldry, Laurent; Duchêne, Cédric; Prudat, Yann; Meuli, Reto; Murray, Micah M.; Vesin, Jean‐Marc

doi:10.1016/j.jneumeth.2009.10.018

Cited by 34 publications

(18 citation statements)

References 35 publications

(35 reference statements)

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“…While evidence is increasingly highlighting the role of oscillatory activity in multisensory phenomena (Lakatos et al, 2007(Lakatos et al, , 2008(Lakatos et al, , 2009Senkowski et al, 2008), such signal analysis methods have yet to be optimized for application to single-trial source estimations based on scalp-recorded EEG (Van Zaen et al, 2010;Ramírez et al, 2011). Such notwithstanding, our results therefore indicate there to be phase-locked and stimulus-locked activities at early poststimulus latencies that exhibit nonlinear multisensory interactions.…”

Section: Mechanisms Subserving the Integration Of Looming Signalsmentioning

confidence: 75%

Looming Signals Reveal Synergistic Principles of Multisensory Integration

Cappe¹,

Thelen²,

Romei³

et al. 2012

J. Neurosci.

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Multisensory interactions are a fundamental feature of brain organization. Principles governing multisensory processing have been established by varying stimulus location, timing and efficacy independently. Determining whether and how such principles operate when stimuli vary dynamically in their perceived distance (as when looming/receding) provides an assay for synergy among the above principles and also means for linking multisensory interactions between rudimentary stimuli with higher-order signals used for communication and motor planning. Human participants indicated movement of looming or receding versus static stimuli that were visual, auditory, or multisensory combinations while 160-channel EEG was recorded. Multivariate EEG analyses and distributed source estimations were performed. Nonlinear interactions between looming signals were observed at early poststimulus latencies (ϳ75 ms) in analyses of voltage waveforms, global field power, and source estimations. These looming-specific interactions positively correlated with reaction time facilitation, providing direct links between neural and performance metrics of multisensory integration. Statistical analyses of source estimations identified looming-specific interactions within the right claustrum/insula extending inferiorly into the amygdala and also within the bilateral cuneus extending into the inferior and lateral occipital cortices. Multisensory effects common to all conditions, regardless of perceived distance and congruity, followed (ϳ115 ms) and manifested as faster transition between temporally stable brain networks (vs summed responses to unisensory conditions). We demonstrate the early-latency, synergistic interplay between existing principles of multisensory interactions. Such findings change the manner in which to model multisensory interactions at neural and behavioral/perceptual levels. We also provide neurophysiologic backing for the notion that looming signals receive preferential treatment during perception.

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Section: Mechanisms Subserving the Integration Of Looming Signalsmentioning

confidence: 75%

Looming Signals Reveal Synergistic Principles of Multisensory Integration

Cappe¹,

Thelen²,

Romei³

et al. 2012

J. Neurosci.

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“…This type of mechanism has been postulated to explain superadditive nonlinear interactions between auditory and somatosensory stimuli (Lakatos et al, 2007). Combining source estimations with single-trial time-frequency analyses and causality analyses would allow for evaluating this model in humans, but still requires additional methodological developments (Gonzalez Andino et al, 2005;Van Zaen et al, 2010). In addition, it will be critical to reconcile how such phase-resetting mechanisms generate superadditive versus subadditive interactions and whether such is contingent on the sensory modalities tested.…”

Section: Discussionmentioning

confidence: 99%

Auditory–Visual Multisensory Interactions in Humans: Timing, Topography, Directionality, and Sources

Cappe¹,

Thut²,

Romei³

et al. 2010

J. Neurosci.

131

117

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Current models of brain organization include multisensory interactions at early processing stages and within low-level, including primary, cortices. Embracing this model with regard to auditory-visual (AV) interactions in humans remains problematic. Controversy surrounds the application of an additive model to the analysis of event-related potentials (ERPs), and conventional ERP analysis methods have yielded discordant latencies of effects and permitted limited neurophysiologic interpretability. While hemodynamic imaging and transcranial magnetic stimulation studies provide general support for the above model, the precise timing, superadditive/subadditive directionality, topographic stability, and sources remain unresolved. We recorded ERPs in humans to attended, but task-irrelevant stimuli that did not require an overt motor response, thereby circumventing paradigmatic caveats. We applied novel ERP signal analysis methods to provide details concerning the likely bases of AV interactions. First, nonlinear interactions occur at 60 -95 ms after stimulus and are the consequence of topographic, rather than pure strength, modulations in the ERP. AV stimuli engage distinct configurations of intracranial generators, rather than simply modulating the amplitude of unisensory responses. Second, source estimations (and statistical analyses thereof) identified primary visual, primary auditory, and posterior superior temporal regions as mediating these effects. Finally, scalar values of current densities in all of these regions exhibited functionally coupled, subadditive nonlinear effects, a pattern increasingly consistent with the mounting evidence in nonhuman primates. In these ways, we demonstrate how neurophysiologic bases of multisensory interactions can be noninvasively identified in humans, allowing for a synthesis across imaging methods on the one hand and species on the other.

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“…The authors used adaptive band-pass filters to track the instantaneous fundamental and first harmonic frequency components [10]. The harmonic frequency tracker (HFT), described in [11], is based on time-varying single pole band-pass filters with the following transfer function:…”

Section: Adaptive Organization Indexmentioning

confidence: 99%

An Adaptive Organization Index to Characterize Atrial Fibrillation using Wrist:Type Photoplethysmographic Signals

Fallet

Lemay

Renevey

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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The performance of photoplethysmography (PPG)-based wearable monitors to diagnose atrial fibrillation (AF) remains unknown to date. This study aims at assessing the performance of new indices quantifying the level of organization in PPG signals to diagnose AF. A database made of 18 adult patients undergoing catheter ablation of various cardiac arrhythmias was used. PPG signals were recorded using a wrist-type sensor. A 12-lead ECG was used as gold standard. ECGs were annotated by experts and selected segments were divided into 4 categories: sinus rhythm (SR), regularly paced rhythm (RPR), irregularly paced rhythm (IPR) and AF. The level of organization of the various PPG signals was measured using an adaptive organization index (AOI), defined as the ratio of the power of the fundamental frequency and the first harmonic to the total power of the PPG signal, computed with adaptive band-pass filters. A total of 2806/803/852/287 10-second epochs were considered for AF/SR/RPR/IPR classes. The following mean AOI values were measured: 0.45±0.11 for AF, 0.73±0.19 for SR, 0.78±0.20 for RPR and 0.61±0.19 for IPR classes. Importantly, the AF AOI was significantly smaller than that of the other categories (p<0.001), indicating a higher degree of disorganization.

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Adaptive tracking of EEG oscillations

Cited by 34 publications

References 35 publications

Looming Signals Reveal Synergistic Principles of Multisensory Integration

Looming Signals Reveal Synergistic Principles of Multisensory Integration

Auditory–Visual Multisensory Interactions in Humans: Timing, Topography, Directionality, and Sources

An Adaptive Organization Index to Characterize Atrial Fibrillation using Wrist:Type Photoplethysmographic Signals

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