Tensor-driven extraction of developmental features from varying paediatric EEG datasets

Kinney‐Lang, Eli; Spyrou, Loukianos; Ebied, Ahmed; Chin, Richard; Escudero, Javier

doi:10.1088/1741-2552/aac664

Cited by 18 publications

(37 citation statements)

References 43 publications

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“…This paper follows the tensor notation and definitions outlined in [8], [16]. The relevant notation is summarized presently.…”

Section: A Notation and Definitionsmentioning

confidence: 99%

“…The n-th way (also referred to as domain) matrix of a given tensor A (n) is the corresponding domain-n slice of that tensor. As an example, in a three-way tensor X ∈ R I×J×K with elements x ijk , the domain-1 frontal slice of X is X (1) ∈ R I×JK , the domain-2 lateral slice is X (2) ∈ R J×IK and domain-3 horizontal slice is X (3) ∈ R K×IJ [8].…”

Section: A Notation and Definitionsmentioning

confidence: 99%

“…Tapping into the underlying brain dynamics and neural mechanisms, like neural plasticity, has been a corner stone for these technologies [4]- [6]. Being able to exploit the vast potential of plasticity present during development is a driving force behind expanding these rehabilitation paradigms to paediatric populations [5], [7], [8]. However, the wildly heterogeneous and variable rate of development among children, especially for children in need of neurorehabilitation paradigms, have largely barred the translation of technologies like BCI [5], [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

“…Recently we proposed methodology for tackling such developmental hurdles through exploiting the natural higher-order structure of paediatric electroencephalography (EEG) data via multi-way (i.e. tensor) analysis [7], [8]. In this approach, we utilize advanced signal processing to model the complex multidimensional EEG as a combination of low-rank component matrices in which unique features can be identified and linked to child age [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…tensor) analysis [7], [8]. In this approach, we utilize advanced signal processing to model the complex multidimensional EEG as a combination of low-rank component matrices in which unique features can be identified and linked to child age [7], [8]. The outlined tensor framework provided a foundation for identifying features with respect to child age, but did not take into account other potential measures of 'development', e.g.…”

Section: Introductionmentioning

confidence: 99%

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Introducing the Joint EEG-Development Inference (JEDI) Model: A Multi-Way, Data Fusion Approach for Estimating Paediatric Developmental Scores via EEG

Kinney‐Lang

Ebied

Auyeung

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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Accounting for developmental changes in children is a key consideration for adapting neurorehabilitation technologies to paediatric populations. Using well-established clinical tests and questionnaires can be resource and time intensive. With many data-driven rehabilitation approaches relying on EEG data, a means to rapidly assess and infer developmental status of children directly from these recordings could be critical. This manuscript proposes a new model for estimating classic developmental diagnostic scores by exploiting data fusion in a joint tensor-matrix decomposition of the EEG and score data. We have designated this model the Joint EEG-Development Inference (JEDI) model. The proposed model is illustrated using a common EEG task (button press) via publicly available paediatric data from pre-adolescent children. Using 3 distinct recording blocks for training, validation and testing and a 10-fold cross-validation scheme, a robust experimental design was used to evaluate the JEDI model under various conditions. Results indicate that the JEDI model can estimate the developmental scores of children while maintaining a high degree of similarity at a population level. These results highlight the JEDI model as a potential evolving tool for rapidly assessing child's development. Clinically, the proposed model could provide useful developmental information in a convenient and low resource manner.

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“…This paper follows the tensor notation and definitions outlined in [8], [16]. The relevant notation is summarized presently.…”

Section: A Notation and Definitionsmentioning

confidence: 99%

Section: A Notation and Definitionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Introducing the Joint EEG-Development Inference (JEDI) Model: A Multi-Way, Data Fusion Approach for Estimating Paediatric Developmental Scores via EEG

Kinney‐Lang

Ebied

Auyeung

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

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Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis

Chatzichristos

Kofidis

Paesschen

et al. 2021

Human Brain Mapping

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Data fusion refers to the joint analysis of multiple datasets that provide different (e.g., complementary) views of the same task. In general, it can extract more information than separate analyses can. Jointly analyzing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measurements has been proved to be highly beneficial to the study of the brain function, mainly because these neuroimaging modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The EEG-fMRI fusion methods that have been reported so far ignore the underlying multiway nature of the data in at least one of the modalities and/or rely on very strong assumptions concerning the relation of the respective datasets. For example, in multisubject analysis, it is commonly assumed that the hemodynamic response function is a priori known for all subjects and/or the coupling across corresponding modes is assumed to be exact (hard). In this article, these two limitations are overcome by adopting tensor models for both modalities and by following soft and flexible coupling approaches to implement the multimodal fusion. The obtained results are compared against those of parallel independent component analysis and hard coupling alternatives, with both synthetic and real data (epilepsy and visual oddball paradigm). Our results demonstrate the clear advantage of using soft and flexible coupled tensor decompositions in scenarios that do not conform with the hard coupling assumption.

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Identifying the Risk in Lie Detection for Assessing Guilty and Innocent Subjects for Healthcare Applications

Nagale,

Khandare

2024

Engineering Cyber-Physical Systems and Critical Infrastructures

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Tensor-driven extraction of developmental features from varying paediatric EEG datasets

Abstract: The described methods are a promising tool for identifying latent developmental features occurring throughout childhood EEG.

Cited by 18 publications

References 43 publications

Introducing the Joint EEG-Development Inference (JEDI) Model: A Multi-Way, Data Fusion Approach for Estimating Paediatric Developmental Scores via EEG

Introducing the Joint EEG-Development Inference (JEDI) Model: A Multi-Way, Data Fusion Approach for Estimating Paediatric Developmental Scores via EEG

Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis

Identifying the Risk in Lie Detection for Assessing Guilty and Innocent Subjects for Healthcare Applications

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