Characterization of spatiotemporal dynamics in EEG data during picture naming with optical flow patterns

V. Volpert; B. Xu; A. Tchechmedjiev; S. Harispe; A. Aksenov; Q. Mesnildrey; A. Beuter; V. Volpert; B. Xu; A. Tchechmedjiev; S. Harispe; A. Aksenov; Q. Mesnildrey; A. Beuter

doi:10.3934/mbe.2023507

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 11429-11463. doi: 10.3934/mbe.2023507

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Characterization of spatiotemporal dynamics in EEG data during picture naming with optical flow patterns

1.
Institut Camille Jordan, UMR 5208 CNRS, University Lyon 1, 69622 Villeurbanne, France
2.
EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Ales, France
3.
CorStim SAS, Montpellier, France

Academic Editor: Xiang-Sheng Wang

Received: 15 February 2023 Revised: 11 April 2023 Accepted: 17 April 2023 Published: 27 April 2023

In this study, we investigate the spatiotemporal dynamics of the neural oscillations by analyzing the electric potential that arises from neural activity. We identify two types of dynamics based on the frequency and phase of oscillations: standing waves or as out-of-phase and modulated waves, which represent a combination of standing and moving waves. To characterize these dynamics, we use optical flow patterns such as sources, sinks, spirals and saddles. We compare analytical and numerical solutions with real EEG data acquired during a picture-naming task. Analytical approximation of standing waves helps us to establish some properties of pattern location and number. Specifically, sources and sinks are mainly located in the same location, while saddles are positioned between them. The number of saddles correlates with the sum of all the other patterns. These properties are confirmed in both the simulated and real EEG data. In particular, source and sink clusters in the EEG data overlap with each other with median percentages around 60%, and hence have high spatial correlation, while source/sink clusters overlap with saddle clusters in less than 1%, and have different locations. Our statistical analysis showed that saddles account for about 45% of all patterns, while the remaining patterns are present in similar proportions.
- human EEG data,
- spatiotemporal patterns,
- optical flow methods,
- picture-naming task
Citation: V. Volpert, B. Xu, A. Tchechmedjiev, S. Harispe, A. Aksenov, Q. Mesnildrey, A. Beuter. Characterization of spatiotemporal dynamics in EEG data during picture naming with optical flow patterns[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 11429-11463. doi: 10.3934/mbe.2023507

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Abstract

In this study, we investigate the spatiotemporal dynamics of the neural oscillations by analyzing the electric potential that arises from neural activity. We identify two types of dynamics based on the frequency and phase of oscillations: standing waves or as out-of-phase and modulated waves, which represent a combination of standing and moving waves. To characterize these dynamics, we use optical flow patterns such as sources, sinks, spirals and saddles. We compare analytical and numerical solutions with real EEG data acquired during a picture-naming task. Analytical approximation of standing waves helps us to establish some properties of pattern location and number. Specifically, sources and sinks are mainly located in the same location, while saddles are positioned between them. The number of saddles correlates with the sum of all the other patterns. These properties are confirmed in both the simulated and real EEG data. In particular, source and sink clusters in the EEG data overlap with each other with median percentages around 60%, and hence have high spatial correlation, while source/sink clusters overlap with saddle clusters in less than 1%, and have different locations. Our statistical analysis showed that saddles account for about 45% of all patterns, while the remaining patterns are present in similar proportions.

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