Riemannian Geometry on Connectivity for Clinical BCI

Corsi, Marie‐Constance; Yger, Florian; Chevallier, Sylvain; Noûs, Camille

doi:10.1109/icassp39728.2021.9414790

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…We validated it on numerous publicly available datasets (Corsi et al, 2022) . Such an approach notably ranked 1st in a clinical challenge that consisted in discriminating mental states from data obtained from stroke patients (Corsi et al, 2021). Future work will consist in considering this type of approach to enrich the information of interest used to discriminate diseases.…”

Section: Discussionmentioning

confidence: 99%

Magnetoencephalography-based interpretable automated differential diagnosis in neurodegenerative diseases

Klepachevskyi,

Romano,

Aristimunha

et al. 2024

Preprint

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Automating the diagnostic process steps has been of interest for research grounds and to help manage the healthcare systems. Improved classification accuracies, provided by ever more sophisticated algorithms, were mirrored by the loss of interpretability on the criteria for achieving accuracy. In other words, the mechanisms responsible for generating the distinguishing features are typically not investigated. Furthermore, the vast majority of the classification studies focus on the classification of one disease as opposed to matched controls. While this scenario has internal validity, concerning the appropriateness toward answering scientific questions, it does not have external validity. In other words, differentiating multiple diseases at once is a classification problem closer to many real-world scenarios. In this work, we test the hypothesis that specific data features hold most of the discriminative power across multiple neurodegenerative diseases. Furthermore, we perform an explorative analysis to compare metrics based on different assumptions (concerning the underlying mechanisms). To test this hypothesis, we leverage a large Magnetoencephalography dataset (N=109) merging four cohorts, recorded in the same clinical setting, of patients affected by multiple sclerosis, amyotrophic lateral sclerosis, Parkinson’s disease, and mild cognitive impairment. Our results show that it is possible to reach a balanced accuracy of 67,1% (chance level = 35%), based on a small set of (non-disease specific) features. We show that edge metrics (defined as statistical dependencies between pairs of brain signals) perform better than nodal metrics (considering region while disregarding the interactions. Moreover, phase-based metrics slightly outperform amplitude-based metrics. In conclusion, our work shows that a small set of phase-based connectivity metrics applied to MEG data successfully distinguishes across multiple neurological diseases.

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Section: Discussionmentioning

confidence: 99%

Magnetoencephalography-based interpretable automated differential diagnosis in neurodegenerative diseases

Klepachevskyi,

Romano,

Aristimunha

et al. 2024

Preprint

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“…The Riemannian approaches present many advantages: they can be applied to all BCI paradigms, no parameter tuning is required, they are robust to noise, and, combined to transfer learning methods, they can lead to calibration-free BCI sessions [81]. In particular, Riemannian geometry-based methods [80,82] are now the state of the art in terms of performance [27] and have won several data competitions 2 [83].…”

Section: Current Challenges and Perspectivesmentioning

confidence: 99%

Electroencephalography and Magnetoencephalography

Corsi

2023

Neuromethods

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In this chapter, we present the main characteristics of electroencephalography (EEG) and magnetoencephalography (MEG). More specifically, this chapter is dedicated to the presentation of the data, the way they can be acquired and analyzed. Then, we present the main features that can be extracted and their applications for brain disorders with concrete examples to illustrate them. Additional materials associated with this chapter are available in the dedicated Github repository.

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“…The Riemannian approaches present many advantages: they can be applied to all BCI paradigms, no parameter tuning is required, they are robust to noise and, combined to transfer learning methods, they can lead to calibration-free BCI sessions [81]. In particular, Riemannian geometry-based methods [80,82] are now the state-of-the-art is terms of performance [27] and have won several data competitions 2 [83].…”

Section: Current Challenges and Perspectivesmentioning

confidence: 99%