Data-driven spectral decomposition of ECoG signal from an auditory oddball experiment in a marmoset monkey: Implications for EEG data in humans

Marrouch, Natasza; Read, Heather L.; Sławińska, Joanna; Giannakis, Dimitrios

doi:10.1109/ijcnn.2018.8489475

Cited by 3 publications

(3 citation statements)

References 38 publications

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“…Consistent with [42], we considered the first tone "deviant" and the last tone "standard". To validate the Koopman operator framework, we replicated and described findings presented by Komatsu and colleagues [42] in [47]. In this paper, consistent with past research highlighting the importance of both Beta and Gamma frequency oscillations in response to unexpected auditory stimuli [37], we extend our earlier work [47] describing two of the extracted modes that fall in the Beta and Gamma frequency range.…”

Section: Data Descriptionsupporting

confidence: 81%

“…This dataset contains a total of 89 tones with tonal frequency ranging form 250 Hz to 4000 Hz. Similarly to [47], this selection is arbitrary but allows us to obtain a balance between a sufficient number of samples and computational cost related to subsequent analysis.…”

Section: Default Setup and Robustness Of Resultsmentioning

confidence: 99%

“…There are several important similarities between spatiotemporal climatic data and multi-channel ECoG recording of brain potentials, including a two-dimensional surface for sampling the system, as well as meaningful signals that vary on multiple spatial and temporal scales. Here, we apply this approach and describe two of the extracted modes, previously shown to characterize neural responses to surprising sensory events [47,48].…”

Section: Data-driven Approach To Computational Neurosciencementioning

confidence: 99%

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Data-driven Koopman operator approach for computational neuroscience

Marrouch

Sławińska

Giannakis

et al. 2019

Ann Math Artif Intell

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This article presents a novel, nonlinear, data-driven signal processing method, which can help neuroscience researchers visualize and understand complex dynamical patterns in both time and space. Specifically, we present applications of a Koopman operator approach for eigendecomposition of electrophysiological signals into orthogonal, coherent components and examine their associated spatiotemporal dynamics. This approach thus provides enhanced capabilities over conventional computational neuroscience tools restricted to analyzing signals in either the time or space domains. This is achieved via machine learning and kernel methods for data-driven approximation of skew-product dynamical systems. The approximations successfully converge to theoretical values in the limit of long embedding windows. First, we describe the method, then using electrocorticographic (ECoG) data from a mismatch negativity experiment, we extract time-separable frequencies without bandpass filtering or prior selection of wavelet features. Finally, we discuss in detail two of the extracted components, Beta (∼ 13 Hz) and high Gamma (∼ 50 Hz) frequencies, and explore the spatiotemporal dynamics of high-and low-frequency components.

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Section: Data Descriptionsupporting

confidence: 81%

Section: Default Setup and Robustness Of Resultsmentioning

confidence: 99%

Section: Data-driven Approach To Computational Neurosciencementioning

confidence: 99%

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Data-driven Koopman operator approach for computational neuroscience

Marrouch

Sławińska

Giannakis

et al. 2019

Ann Math Artif Intell

Self Cite

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Spatiotemporal Pattern Extraction by Spectral Analysis of Vector-Valued Observables

et al. 2019

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We present a data-driven framework for extracting complex spatiotemporal patterns generated by ergodic dynamical systems. Our approach, called Vector-valued Spectral Analysis (VSA), is based on an eigendecomposition of a kernel integral operator acting on a Hilbert space of vector-valued observables of the system, taking values in a space of functions (scalar fields) on a spatial domain. This operator is constructed by combining aspects of the theory of operator-valued kernels for machine learning with delay-coordinate maps of dynamical systems. In contrast to conventional eigendecomposition techniques, which decompose the input data into pairs of temporal and spatial modes with a separable, tensor product structure, the patterns recovered by VSA can be manifestly non-separable, requiring only a modest number of modes to represent signals with intermittency in both space and time. Moreover, the kernel construction naturally quotients out dynamical symmetries in the data, and exhibits an asymptotic commutativity property with the Koopman evolution operator of the system, enabling decomposition of multiscale signals into dynamically intrinsic patterns. Application of VSA to the Kuramoto-Sivashinsky model demonstrates significant performance gains in efficient and meaningful decomposition over eigendecomposition techniques utilizing scalar-valued kernels.

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Attractor-Like Dynamics Extracted from Human Electrocorticographic Recordings Underlie Computational Principles of Auditory Bistable Perception

Melland

Curtu

2023

J. Neurosci.

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In bistable perception, observers experience alternations between two interpretations of an unchanging stimulus. Neurophysiological studies of bistable perception typically partition neural measurements into stimulus-based epochs and assess neuronal differences between epochs based on subjects’ perceptual reports. Computational studies replicate statistical properties of percept durations with modeling principles like competitive attractors or Bayesian inference. However, bridging neuro-behavioral findings with modeling theory requires the analysis of single-trial dynamic data. Here, we propose an algorithm for extracting non-stationary timeseries features from single-trial electrocorticography (ECoG) data. We applied the proposed algorithm to 5-minute ECoG recordings from human primary auditory cortex obtained during perceptual alternations in an auditory triplet streaming task (six subjects: 4 male, 2 female). We report two ensembles of emergent neuronal features in all trial blocks. One ensemble consists of periodic functions that encode a stereotypical response to the stimulus. The other comprises more transient features and encodes dynamics associated with bistable perception at multiple time scales: minutes (within-trial alternations), seconds (duration of individual percepts), and milliseconds (switches between percepts). Within the second ensemble, we identified a slowly drifting rhythm that correlates with the perceptual states and several oscillators with phase shifts near perceptual switches. Projections of single-trial ECoG data onto these features establish low-dimensional attractor-like geometric structures invariant across subjects and stimulus types. These findings provide supporting neural evidence for computational models with oscillatory-driven attractor-based principles. The feature extraction techniques described here generalize across recording modality and are appropriate when hypothesized low-dimensional dynamics characterize an underlying neural system.Significance StatementIrrespective of the sensory modality, neurophysiological studies of multi-stable perception have typically investigated events time-locked to the perceptual switching rather than the time course of the perceptual states per se. Here, we propose an algorithm that extracts neuronal features of bistable auditory perception from largescale single-trial data while remaining agnostic to the subject’s perceptual reports. The algorithm captures the dynamics of perception at multiple timescales—minutes (within-trial alternations), seconds (durations of individual percepts), and milliseconds (timing of switches)—and distinguishes attributes of neural encoding of the stimulus from those encoding the perceptual states. Finally, our analysis identifies a set of latent variables that exhibit alternating dynamics along a low-dimensional manifold, similar to trajectories in attractor-based models for perceptual bistability.

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Data-driven spectral decomposition of ECoG signal from an auditory oddball experiment in a marmoset monkey: Implications for EEG data in humans

Cited by 3 publications

References 38 publications

Data-driven Koopman operator approach for computational neuroscience

Data-driven Koopman operator approach for computational neuroscience

Spatiotemporal Pattern Extraction by Spectral Analysis of Vector-Valued Observables

Attractor-Like Dynamics Extracted from Human Electrocorticographic Recordings Underlie Computational Principles of Auditory Bistable Perception

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