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Transfer Learning Algorithm of P300-EEG Signal Based on XDAWN Spatial Filter and Riemannian Geometry Classifier
2020
Applied Sciences
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Therefore, the transfer learning method proposed in this article, which combines XDAWN spatial filter and Riemannian Geometry classifier (RGC), can achieve offline cross-subject transfer learning in the ...
Then, the Riemannian Geometry Mean (RGM) is used as the reference matrix to perform the affine transformation of the symmetric positive definite (SPD) covariance matrix calculated from the filtered signal ...
Project M * into the Riemannian manifold, and calculate the Riemannian Geometry mean points for two classes, G1 and G2. 8. end for 9. ...
doi:10.3390/app10051804
fatcat:6uvf4cpqwndvhofubbajxdleey
Riemannian Approaches in Brain-Computer Interfaces: A Review
2017
IEEE transactions on neural systems and rehabilitation engineering
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representation and learning, classifier design and calibration time reduction. ...
This article, after a quick introduction to Riemannian geometry and a presentation of the BCI-relevant manifolds, reviews how these approaches have been used for EEG-based BCI, in particular for feature ...
ACKNOWLEDGMENT The authors acknowledge the support of the Inria Project Lab BCI-LIFT and the French National Research Agency (REBEL project and grant ANR-15-CE23-0013-01). ...
doi:10.1109/tnsre.2016.2627016
pmid:27845666
fatcat:gcyv36lkyzbk5ptfxp7sq5dkoe
Using Riemannian geometry for SSVEP-based Brain Computer Interface
[article]
2015
arXiv
pre-print
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Riemannian geometry has been applied to Brain Computer Interface (BCI) for brain signals classification yielding promising results. ...
This paper proposes a comprehensive review of the actual tools of information geometry and how they could be applied on covariance matrices of EEG. ...
Subject with lowest BCI performance, (a) before and (b) after Riemannian potato filtering. Subject with highest BCI performance, (c) before and (d) after Riemannian potato filtering. ...
arXiv:1501.03227v3
fatcat:qjsbwflnpjdb5l34xmgqabw3va
Monte Carlo Tracking on the Riemannian Manifold of Multivariate Normal Distributions
2009
2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop
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In this contribution, a general scheme of particle filtering on Riemannian manifolds is proposed. ...
The Riemannian manifold formulation of the state space model avoids the curse of dimensionality from which suffers most of the particle filter methods. ...
These concepts are necessary to the design of the particle filter on Riemannian manifolds (Section 4). For further details on Riemannian geometry, refer to [7] . ...
doi:10.1109/dsp.2009.4785935
fatcat:zabcd4suwnhrfbdof3parjzj4a
Transfer Learning for SSVEP-based BCI Using Riemannian Similarities Between Users
2018
2018 26th European Signal Processing Conference (EUSIPCO)
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A first attempt to leverage this issue is to design user-specific spatial filters, carefully adjusted with a lengthy calibration phase. ...
This method is evaluated on 12 subjects performing an SSVEP task for the control of an exoskeleton arm and the results show the contribution of Riemannian geometry and of the user-specific composite mean ...
In that case, there is no need to estimate session-and userspecific spatial filters as they are encompassed in the geometry of the considered space. ...
doi:10.23919/eusipco.2018.8553441
dblp:conf/eusipco/KalungaCB18
fatcat:6dueqgpylzgz3oqic6gquvxlqi
Particle Filtering on Riemannian Manifolds
2006
AIP Conference Proceedings
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The popularity of the particle filter method stems from its simplicity and flexibility to deal with non linear/non Gaussian dynamical models. ...
In this contribution, we propose an implementation of the particle filter with the constraint that the system state lies in a low dimensional Riemannian manifold. ...
Only few recent papers have tried to use the geometry of the manifold to design learning algorithms [2, 3] . ...
doi:10.1063/1.2423278
fatcat:6ikwtbnkwnao5npjxq7uyrmhke
Dimensionality reduction based on distance preservation to local mean for symmetric positive definite matrices and its application in brain–computer interfaces
2017
Journal of Neural Engineering
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In this paper, we propose a nonlinear dimensionality reduction algorithm for the manifold of Symmetric Positive Definite (SPD) matrices that considers the geometry of SPD matrices and provides a low dimensional ...
The proposed algorithm, tries to preserve the local structure of the data by preserving distance to local mean (DPLM) and also provides an implicit projection matrix. ...
We compare it with the results achieved over fixed time window and band-pass filter (showed by a "fixed" postfix) in terms of kappa value. ...
doi:10.1088/1741-2552/aa61bb
pmid:28220764
fatcat:nz3ritn5efg4rbnwepem27e25u
Online SSVEP-based BCI using Riemannian geometry
2016
Neurocomputing
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Nonetheless, existing classification algorithms in Riemannian spaces are designed for offline analysis. ...
Working in Euclidean space with covariance matrices is known to be error-prone, one might take advantage of algorithmic advances in Riemannian geometry and matrix manifold to implement methods for Symmetric ...
Fixed point covariance matrix estimator The Fixed Point Covariance Matrix [71] is based on the maximum likelihood estimatorl which is a solution to the following equation: Σ fp ¼l ¼ C N X N n ¼ 1 ðx ...
doi:10.1016/j.neucom.2016.01.007
fatcat:46w5aj7anzdibobgadmkduvhlm
Adaptive Error Detection Method for P300-based Spelling Using Riemannian Geometry
2016
International Journal of Advanced Computer Science and Applications
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In order to address above stated challenge this research proposes a system so called Adaptive Error Detection method for P300-Based Spelling using Riemannian Geometry, the system comprises of three main ...
In second step most relevant features are extracted using xDAWN spatial filtering along with covariance matrices for handling high dimensional data and in final step elastic net classification algorithm ...
The information geometry is a field of mathematics, which takes probability distributions as points of a Riemannian manifold (Manifold has resemblance to homemorphic euclidean space near each point.). ...
doi:10.14569/ijacsa.2016.071143
fatcat:wow5x6svibexdbpvmarazmvbre
Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures
2012
2012 IEEE Workshop on the Applications of Computer Vision (WACV)
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For example, only distances between points to the tangent pole are equal to true geodesic distances. This is restrictive and may lead to inaccurate modelling. ...
Experiments on several visual classification tasks (gesture recognition, person re-identification and texture classification) show that in comparison to tangentbased processing and state-of-the-art methods ...
The proposed RLPP method is compared with Histogram Plus Epitome (HPE) [1] , Symmetry-Driven Accumulation of Local Features (SDALF) [7] and Partial Least Squares (PLS) [23] . ...
doi:10.1109/wacv.2012.6163005
dblp:conf/wacv/HarandiSWL12
fatcat:pxqe4xpwr5cono7is7omhi6xqm
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features
[article]
2018
arXiv
pre-print
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The Riemannian covariance features outperform the CSP by achieving 74.27±15.5 and 4x faster in testing. Using more temporal windows for Riemannian features results in 75.47±12.8 ...
We focus on the well-known common spatial pattern (CSP) and Riemannian covariance methods, and significantly extend these two feature extractors to multiscale temporal and spectral cases. ...
ACKNOWLEDGMENTS Support was received from the ETH Zurich Postdoctoral Fellowship program and the Marie Curie Actions for People COFUND Program. ...
arXiv:1806.06823v3
fatcat:jhwgggczafg7fc2sov7i22gvhi
An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training
2021
Frontiers in Human Neuroscience
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The subjects learned to modulate their sensorimotor rhythm to centralize the points within one category and to separate points belonging to different categories. ...
This paper proposes an online data visualization feedback protocol that intuitively reflects the EEG distribution in Riemannian geometry in real time. ...
analysis and as well as wrote the manuscript at all stages. SYX and XX revised the manuscript at all stages. KO revised the manuscript. XD, YC, and ZW conducted the experiments. ...
doi:10.3389/fnhum.2021.625983
pmid:34163337
pmcid:PMC8215169
fatcat:vxalc77vlzford27pq2be2cazq
A New Subject-Specific Discriminative and Multi-Scale Filter Bank Tangent Space Mapping Method for Recognition of Multiclass Motor Imagery
2021
Frontiers in Human Neuroscience
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In order to solve the problem, a discriminative and multi-scale filter bank tangent space mapping (DMFBTSM) algorithm is proposed in this article to design the subject-specific Filter Bank (FB) so as to ...
Then TSM algorithm is used to estimate Riemannian tangent space features in each sub-band and finally a liner Support Vector Machines (SVM) is used for classification.Main Results: The analysis results ...
FUNDING This research was supported by grants from the National Natural Science Foundation of China projects (NSFC, Nos. 81771926, 61763022, 81470084, 61463024, and 62006246). ...
doi:10.3389/fnhum.2021.595723
pmid:33762911
pmcid:PMC7982728
fatcat:lmycfe6r4zen3igdst5547xcfi
A Plug&Play P300 BCI Using Information Geometry
[article]
2014
arXiv
pre-print
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Through a new estimation of covariance matrices, this work extend the use of Riemannian geometry, which was previously limited to SMR-based BCI, to the problem of classification of ERPs. ...
This paper presents a new classification methods for Event Related Potentials (ERP) based on an Information geometry framework. ...
The information geometry is a field of information theory where the probability distributions are taken as point of a Riemannian manifold. This field has been popularised by S. ...
arXiv:1409.0107v1
fatcat:djolu776bnhklcdjppnl25dh7u
Riemannian Classification for SSVEP-Based BCI
[chapter]
2018
Brain–Computer Interfaces Handbook
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A recent successful approach in feature extraction and signal processing for BCI is Riemannian geometry, which deals with covariance matrices. ...
With an application to SSVEP, this article shows through a comparison how Riemannian geometry allows one to easily define offline and online implementations that have better accuracies than state of the ...
, namely the Riemannian geometry. ...
doi:10.1201/9781351231954-19
fatcat:ckjfdf3iardazl45f4vlypf72m
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