Journal article
Modeling Complex EEG Data Distribution on the Riemannian Manifold Toward Outlier Detection and Multimodal Classification
IEEE transactions on biomedical engineering, v 71(2), pp 377-387
01 Feb 2024
PMID: 37450357
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Objective: The usage of Riemannian geometry for Brain-computer interfaces (BCIs) has gained momentum in recent years. Most of the machine learning techniques proposed for Riemannian BCIs consider the data distribution on a manifold to be unimodal. However, the distribution is likely to be multimodal rather than unimodal since high-data variability is a crucial limitation of electroencephalography (EEG). In this paper, we propose a novel data modeling method for considering complex data distributions on a Riemannian manifold of EEG covariance matrices, aiming to improve BCI reliability. Methods: Our method, Riemannian spectral clustering (RiSC), represents EEG covariance matrix distribution on a manifold using a graph with proposed similarity measurement based on geodesic distances, then clusters the graph nodes through spectral clustering. This allows flexibility to model both a unimodal and a multimodal distribution on a manifold. RiSC can be used as a basis to design an outlier detector named outlier detection Riemannian spectral clustering (odenRiSC) and a multimodal classifier named multimodal classifier Riemannian spectral clustering (mcRiSC). All required parameters of odenRiSC/mcRiSC are selected in data-driven manner. Moreover, there is no need to pre-set a threshold for outlier detection and the number of modes for multimodal classification. Results: The experimental evaluation revealed odenRiSC can detect EEG outliers more accurately than existing methods and mcRiSC outperformed the standard unimodal classifier, especially on high-variability datasets. Conclusion: odenRiSC/mcRiSC are anticipated to contribute to making real-life BCIs outside labs and neuroergonomics applications more robust. Significance: RiSC can work as a robust EEG outlier detector and multimodal classifier.
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Details
- Title
- Modeling Complex EEG Data Distribution on the Riemannian Manifold Toward Outlier Detection and Multimodal Classification
- Creators
- Maria Sayu Yamamoto - Centre Inria de SaclayKhadijeh Sadatnejad - Institut national de recherche en sciences et technologies du numériqueToshihisa Tanaka - Tokyo University of Agriculture and TechnologyMd. Rabiul Islam - Cleveland ClinicFrederic Dehais - Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO)Yuichi Tanaka - The University of OsakaFabien Lotte - Institut national de recherche en sciences et technologies du numérique
- Publication Details
- IEEE transactions on biomedical engineering, v 71(2), pp 377-387
- Publisher
- IEEE
- Number of pages
- 11
- Grant note
- ANR-20-THIA-0013-01 / Agence Nationale de la Recherche; French National Research Agency (10.13039/501100001665) JPMJKB2307 / Advanced International Collaborative Research Japan Science and Technology Agency (10.13039/501100002241) 17H01760 / JSPS KAKENHI ERC-2016-STG-714567 / European Research Council (10.13039/501100000781)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering and Science
- Web of Science ID
- WOS:001204223700022
- Scopus ID
- 2-s2.0-85164793839
- Other Identifier
- 991022202511604721
UN Sustainable Development Goals (SDGs)
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Engineering, Biomedical