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Hardware and software description used for signals acquisition for gait applications.
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Distance to mean representation of the <t>EEG</t> <t>data</t> from the actor of the same representative couple as in using Euclidean (A) and Riemannian (B) distances and from the same representative observer using Euclidean (C) and Riemannian (D) distances. The Riemannian distance δ R is defined in Eq. 5 and the Euclidean distance δ E is defined as the Frobenius norm of the difference between the two covariance matrices. The horizontal (vertical) axis represents the distance δ(Σ i , Σ F ) (δ(Σ i , Σ V )) between a covariance matrix Σ i and the mean covariance matrix of the festive (violent) class Σ F (Σ V ). The straight dashed line represents the function δ(Σ i , Σ F ) = δ(Σ i , Σ V ). Unlike Euclidean metrics, Riemannian metrics were able to convincingly separate the violent and festive classes.
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Distance to mean representation of the <t>EEG</t> <t>data</t> from the actor of the same representative couple as in using Euclidean (A) and Riemannian (B) distances and from the same representative observer using Euclidean (C) and Riemannian (D) distances. The Riemannian distance δ R is defined in Eq. 5 and the Euclidean distance δ E is defined as the Frobenius norm of the difference between the two covariance matrices. The horizontal (vertical) axis represents the distance δ(Σ i , Σ F ) (δ(Σ i , Σ V )) between a covariance matrix Σ i and the mean covariance matrix of the festive (violent) class Σ F (Σ V ). The straight dashed line represents the function δ(Σ i , Σ F ) = δ(Σ i , Σ V ). Unlike Euclidean metrics, Riemannian metrics were able to convincingly separate the violent and festive classes.
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Distance to mean representation of the <t>EEG</t> <t>data</t> from the actor of the same representative couple as in using Euclidean (A) and Riemannian (B) distances and from the same representative observer using Euclidean (C) and Riemannian (D) distances. The Riemannian distance δ R is defined in Eq. 5 and the Euclidean distance δ E is defined as the Frobenius norm of the difference between the two covariance matrices. The horizontal (vertical) axis represents the distance δ(Σ i , Σ F ) (δ(Σ i , Σ V )) between a covariance matrix Σ i and the mean covariance matrix of the festive (violent) class Σ F (Σ V ). The straight dashed line represents the function δ(Σ i , Σ F ) = δ(Σ i , Σ V ). Unlike Euclidean metrics, Riemannian metrics were able to convincingly separate the violent and festive classes.
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Distance to mean representation of the <t>EEG</t> <t>data</t> from the actor of the same representative couple as in using Euclidean (A) and Riemannian (B) distances and from the same representative observer using Euclidean (C) and Riemannian (D) distances. The Riemannian distance δ R is defined in Eq. 5 and the Euclidean distance δ E is defined as the Frobenius norm of the difference between the two covariance matrices. The horizontal (vertical) axis represents the distance δ(Σ i , Σ F ) (δ(Σ i , Σ V )) between a covariance matrix Σ i and the mean covariance matrix of the festive (violent) class Σ F (Σ V ). The straight dashed line represents the function δ(Σ i , Σ F ) = δ(Σ i , Σ V ). Unlike Euclidean metrics, Riemannian metrics were able to convincingly separate the violent and festive classes.
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Distance to mean representation of the <t>EEG</t> <t>data</t> from the actor of the same representative couple as in using Euclidean (A) and Riemannian (B) distances and from the same representative observer using Euclidean (C) and Riemannian (D) distances. The Riemannian distance δ R is defined in Eq. 5 and the Euclidean distance δ E is defined as the Frobenius norm of the difference between the two covariance matrices. The horizontal (vertical) axis represents the distance δ(Σ i , Σ F ) (δ(Σ i , Σ V )) between a covariance matrix Σ i and the mean covariance matrix of the festive (violent) class Σ F (Σ V ). The straight dashed line represents the function δ(Σ i , Σ F ) = δ(Σ i , Σ V ). Unlike Euclidean metrics, Riemannian metrics were able to convincingly separate the violent and festive classes.
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Distance to mean representation of the <t>EEG</t> <t>data</t> from the actor of the same representative couple as in using Euclidean (A) and Riemannian (B) distances and from the same representative observer using Euclidean (C) and Riemannian (D) distances. The Riemannian distance δ R is defined in Eq. 5 and the Euclidean distance δ E is defined as the Frobenius norm of the difference between the two covariance matrices. The horizontal (vertical) axis represents the distance δ(Σ i , Σ F ) (δ(Σ i , Σ V )) between a covariance matrix Σ i and the mean covariance matrix of the festive (violent) class Σ F (Σ V ). The straight dashed line represents the function δ(Σ i , Σ F ) = δ(Σ i , Σ V ). Unlike Euclidean metrics, Riemannian metrics were able to convincingly separate the violent and festive classes.
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Distance to mean representation of the <t>EEG</t> <t>data</t> from the actor of the same representative couple as in using Euclidean (A) and Riemannian (B) distances and from the same representative observer using Euclidean (C) and Riemannian (D) distances. The Riemannian distance δ R is defined in Eq. 5 and the Euclidean distance δ E is defined as the Frobenius norm of the difference between the two covariance matrices. The horizontal (vertical) axis represents the distance δ(Σ i , Σ F ) (δ(Σ i , Σ V )) between a covariance matrix Σ i and the mean covariance matrix of the festive (violent) class Σ F (Σ V ). The straight dashed line represents the function δ(Σ i , Σ F ) = δ(Σ i , Σ V ). Unlike Euclidean metrics, Riemannian metrics were able to convincingly separate the violent and festive classes.
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Distance to mean representation of the <t>EEG</t> <t>data</t> from the actor of the same representative couple as in using Euclidean (A) and Riemannian (B) distances and from the same representative observer using Euclidean (C) and Riemannian (D) distances. The Riemannian distance δ R is defined in Eq. 5 and the Euclidean distance δ E is defined as the Frobenius norm of the difference between the two covariance matrices. The horizontal (vertical) axis represents the distance δ(Σ i , Σ F ) (δ(Σ i , Σ V )) between a covariance matrix Σ i and the mean covariance matrix of the festive (violent) class Σ F (Σ V ). The straight dashed line represents the function δ(Σ i , Σ F ) = δ(Σ i , Σ V ). Unlike Euclidean metrics, Riemannian metrics were able to convincingly separate the violent and festive classes.
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Distance to mean representation of the <t>EEG</t> <t>data</t> from the actor of the same representative couple as in using Euclidean (A) and Riemannian (B) distances and from the same representative observer using Euclidean (C) and Riemannian (D) distances. The Riemannian distance δ R is defined in Eq. 5 and the Euclidean distance δ E is defined as the Frobenius norm of the difference between the two covariance matrices. The horizontal (vertical) axis represents the distance δ(Σ i , Σ F ) (δ(Σ i , Σ V )) between a covariance matrix Σ i and the mean covariance matrix of the festive (violent) class Σ F (Σ V ). The straight dashed line represents the function δ(Σ i , Σ F ) = δ(Σ i , Σ V ). Unlike Euclidean metrics, Riemannian metrics were able to convincingly separate the violent and festive classes.
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Distance to mean representation of the <t>EEG</t> <t>data</t> from the actor of the same representative couple as in using Euclidean (A) and Riemannian (B) distances and from the same representative observer using Euclidean (C) and Riemannian (D) distances. The Riemannian distance δ R is defined in Eq. 5 and the Euclidean distance δ E is defined as the Frobenius norm of the difference between the two covariance matrices. The horizontal (vertical) axis represents the distance δ(Σ i , Σ F ) (δ(Σ i , Σ V )) between a covariance matrix Σ i and the mean covariance matrix of the festive (violent) class Σ F (Σ V ). The straight dashed line represents the function δ(Σ i , Σ F ) = δ(Σ i , Σ V ). Unlike Euclidean metrics, Riemannian metrics were able to convincingly separate the violent and festive classes.
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Image Search Results


Hardware and software description used for signals acquisition for gait applications.

Journal: Frontiers in Human Neuroscience

Article Title: Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

doi: 10.3389/fnhum.2020.613254

Figure Lengend Snippet: Hardware and software description used for signals acquisition for gait applications.

Article Snippet: Contreras-Vidal et al. ( ) , EEG , BrainAmpDC, Brain Products , , , 64 , 1,000 , , Whole scalp , H2 Lower Limb Exoskeleton, EEGLAB toolbox.

Techniques: Software, Sampling

Distance to mean representation of the EEG data from the actor of the same representative couple as in using Euclidean (A) and Riemannian (B) distances and from the same representative observer using Euclidean (C) and Riemannian (D) distances. The Riemannian distance δ R is defined in Eq. 5 and the Euclidean distance δ E is defined as the Frobenius norm of the difference between the two covariance matrices. The horizontal (vertical) axis represents the distance δ(Σ i , Σ F ) (δ(Σ i , Σ V )) between a covariance matrix Σ i and the mean covariance matrix of the festive (violent) class Σ F (Σ V ). The straight dashed line represents the function δ(Σ i , Σ F ) = δ(Σ i , Σ V ). Unlike Euclidean metrics, Riemannian metrics were able to convincingly separate the violent and festive classes.

Journal: Frontiers in Neuroscience

Article Title: Hyperscanning EEG and Classification Based on Riemannian Geometry for Festive and Violent Mental State Discrimination

doi: 10.3389/fnins.2020.588357

Figure Lengend Snippet: Distance to mean representation of the EEG data from the actor of the same representative couple as in using Euclidean (A) and Riemannian (B) distances and from the same representative observer using Euclidean (C) and Riemannian (D) distances. The Riemannian distance δ R is defined in Eq. 5 and the Euclidean distance δ E is defined as the Frobenius norm of the difference between the two covariance matrices. The horizontal (vertical) axis represents the distance δ(Σ i , Σ F ) (δ(Σ i , Σ V )) between a covariance matrix Σ i and the mean covariance matrix of the festive (violent) class Σ F (Σ V ). The straight dashed line represents the function δ(Σ i , Σ F ) = δ(Σ i , Σ V ). Unlike Euclidean metrics, Riemannian metrics were able to convincingly separate the violent and festive classes.

Article Snippet: EEG data of the observers were recorded with 32 channels (Brain Products Brainamp DC with actiCAP) with a resolution of 0.1 μV per bit at a sampling rate of 1,000 Hz.

Techniques: