Maria Sayu Yamamoto

pyRiemann/pyRiemann: v0.10

A. Barachant, Quentin Barthélemy, Gabriel Wagner vom Berg, Alexandre Gramfort et autres

version 0.10

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Addressing the Large Variability of EEG Data with Riemannian Geometry : Toward Designing Reliable Brain-Computer Interfaces

Maria Sayu Yamamoto

Aborder la grande variabilité des données EEG avec la géométrie riemannienne : vers la conception d'interfaces cerveau-ordinateur fiables Les interfaces cerveau-ordinateur (BCI) basées sur la géométrie riemannienne ont gagné en popularité au cours de la dernière décennie, démontrant des améliorations significatives dans …

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Novel SPD Matrix Representations Considering Cross-Frequency Coupling for EEG Classification Using Riemannian Geometry

Maria Sayu Yamamoto, Apolline Mellot, Sylvain Chevallier, Fabien Lotte

Accurate classification of cognitive states from Electroencephalographic (EEG) signals is crucial in neuroscience applications such as Brain-Computer Interfaces (BCIs). Classification pipelines based on Riemannian geometry are often state-of-the-art in the BCI field. In this type of BCI, covariance matrices based on EEG …

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Modeling Complex EEG Data Distribution on the Riemannian Manifold Toward Outlier Detection and Multimodal Classification

Maria Sayu Yamamoto, Khadijeh Sadatnejad, Toshihisa Tanaka, Md. Rabiul Islam et autres

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 …

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Class-distinctiveness-based frequency band selection on the Riemannian manifold for oscillatory activity-based BCIs: preliminary results

Maria Sayu Yamamoto, Fabien Lotte, Florian Yger, Sylvain Chevallier

Considering user-specific settings is known to enhance Brain-Computer Interface (BCI) performances. In particular, the optimal frequency band for oscillatory activity classification is highly user-dependent and many frequency band selection methods have been developed in the past two decades. However, it is not …

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User-specific frequency band and time segment selection with high class distinctiveness for Riemannian BCIs

Maria Sayu Yamamoto, Fabien Lotte, Florian Yger, Sylvain Chevallier

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When should MI-BCI feature optimization include prior knowledge, and which one?

Camille Benaroch, Maria Sayu Yamamoto, Aline Roc, Pauline Dreyer et autres

Motor imagery-based brain–computer interfaces (MI-BCIs) rely on interactions between humans and machines. The (learning) characteristics of both components are key to understand and improve performances. Data-driven methods are often used to select/extract features with very little neurophysiological prior. Should they include prior …

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Reliable outlier detection by spectral clustering on Riemannian manifold of EEG covariance matrix

Maria Sayu Yamamoto, Khadijeh Sadatnejad, Islam, Fabien Lotte et autres

Introduction: Automatically identifying and rejecting artifact-contaminated trials is a key problem to design robust BCIs. Here, we propose a novel outlier detection method based on Riemannian Geometry (RG), a promising approach for BCI classification [1]. With RG, EEG signals are represented and …

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Should frequency band selection algorithms include neurophysiological constraints?

Maria Sayu Yamamoto, Camille Benaroch, Aline Roc, Thibaut Monseigne et autres

National audience

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Subspace Oddity - Optimization on Product of Stiefel Manifolds for EEG Data

Maria Sayu Yamamoto, Florian Yger, Sylvain Chevallier

Dimensionality reduction of high-dimensional electroencephalography (EEG) covariance matrices is crucial for effective utilization of Riemannian geometry in Brain-Computer Interfaces (BCI). In this paper, we propose a novel similarity-based classification method that relies on dimensionality reduction of EEG covariance matrices. Conventionally, the dimension …

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Detecting EEG outliers for BCI on the Riemannian manifold using spectral clustering

Maria Sayu Yamamoto, Khadijeh Sadatnejad, Toshihisa Tanaka, Md. Rabiul Islam et autres

Automatically detecting and removing Electroencephalogram (EEG) outliers is essential to design robust brain-computer interfaces (BCIs). In this paper, we propose a novel outlier detection method that works on the Riemannian manifold of sample covariance matrices (SCMs). Existing outlier detection methods run the …

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[Modulation of experimental myasthenia gravis by IVIg].

Sonia Berrih‐Aknin, Asmae Aissaoui, Maria Sayu Yamamoto, Srini V. Kaveri

Myasthenia Gravis (MG) is an autoimmune disease mediated by antibodies directed against the acetylcholine receptor (AChR). Treatment by IVIg is effective in acute forms of myasthenia gravis. In order to determine the in vivo effects of the various fractions of human immunoglobulins, …

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