Fast Gaussian Naïve Bayes for searchlight classification analysis

Ontivero-Ortega, Marlis; Lage‐Castellanos, Agustín; Valente, Giancarlo; Goebel, Rainer; Valdés-Sosa, Mitchell

doi:10.1016/j.neuroimage.2017.09.001

Cited by 135 publications

(71 citation statements)

References 33 publications

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“…Limb contraction and fear perception 1 Limb contraction not only showed positive correlations to subcortical structures but also to 2 cortical areas. The observed clusters spanned affective, action observation, and motor 3 preparation and execution networks, also known to be involved in the processing of emotional 4 body expressions, especially fearful ones (de Gelder, 2006).…”

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confidence: 94%

“…Each actor's joint positions were estimated using the state-of-the-art 2D 23 pose estimation library OpenPose (Cao, Simon, Wei, & Sheikh, 2017). Quantitative features 24 were derived from the joint 2D positions such as velocity, acceleration, vertical movement, the 25 angles between limbs, symmetry, limb contraction and surface, giving their importance in 1 previous work (for a review see Kleinsmith & Bianchi-Berthouze, 2012). By means of 2 representational similarity multi-voxel pattern analysis techniques, we investigated whether the 3 (dis)similarity of body posture and kinematics between different emotional categories could 4 explain the neural response in and beyond body-selective regions.…”

Section: Introductionmentioning

confidence: 99%

“…The videos used in this 22 experiment were selected from a larger validated stimulus set to ensure high recognition 23 overview of the feature definition procedure, see Table 1. Initially, each feature was calculated 1 for each frame; however, all values were averaged over the duration of the video clip (i.e. 25 2 frames) for their comparison to the imaging data.…”

Section: Introductionmentioning

confidence: 99%

“…shoulder ratio, surface, limb distances, symmetry and limb angles), and kinematic ones (i.e. 1 velocity, acceleration and vertical movement). Subsequently, a paired-sample t-test was 2 conducted per ROI comparing kinematic and postural values.…”

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confidence: 99%

“…Acknowledgements work was supported by the European Research Council (ERC) FP7-IDEAS-ERC (Grant 11 agreement number 295673; Emobodies), by the Future and Emerging Technologies (FET)12 Proactive Programme H2020-EU 1…”

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confidence: 99%

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Limb contraction drives fear perception

Solanas

Vaessen

Gelder³

2020

Preprint

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Running title: Brain representation of computational body features 12 Abstract 1Humans and other primate species are experts at recognizing affective information from body 2 movements but the underlying brain mechanisms are still largely unknown. Previous research 3 focusing on the brain representation of symbolic emotion categories has led to mixed results. 4This study used representational similarity and multi-voxel pattern analysis techniques to 5 investigate how postural and kinematic features computed from affective whole-body 6 movement videos are related to brain processes. We show that body posture and kinematics 7 differentially activated brain regions indicating that this information might be selectively 8 encoded in these regions. Most specifically, the feature limb contraction seemed to be 9 particularly relevant for distinguishing fear and it was represented in several regions spanning 10 affective, action observation and motor preparation networks. Our approach goes beyond 11 traditional methods of mapping symbolic emotion categories to brain activation/deactivation 12 by discovering which specific movement features are encoded in the brain, and possibly drive 13 automatic emotion perception. 14 15 16 17 18 19 20 21 accuracy (>80%). Detailed information regarding the recording and validation of these stimuli 1 can be found in Kret et al. (2011b). 2 Pose estimation 3 The state-of-the-art 2D pose estimation library OpenPose (v1.0.1, Cao et al., 2017) was used to 4 estimate the pose of each actor in the video stimuli. By means of a convolutional neural 5 network, OpenPose estimates the position (i.e. x-and y-coordinates) of a total of 18 keypoints 6 corresponding to the main body joints (i.e. ears, eyes, nose, neck, shoulders, elbows, hands, left 7 and right part of the hip, knees and feet). Subsequently, a skeleton is produced by association 8 of pairs of keypoints using part affinity fields (see Figure 1.A for examples of our stimuli with 9the OpenPose skeleton). In the current study, the keypoints belonging to the eyes and ears were 10 excluded from further analyses since the blurring of the actors' faces often resulted in an 11 inaccurate location estimation. The keypoint corresponding to the nose was kept, however, as a 12 reference for head position. Therefore, the x-and y-coordinates were obtained for a total of 14 13

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confidence: 94%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Limb contraction drives fear perception

Solanas

Vaessen

Gelder³

2020

Preprint

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Automatic fall risk assessment with Siamese network for stroke survivors using inertial sensor‐based signals

Fan

Wang

Zhao

et al. 2022

Int J of Intelligent Sys

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Fall is a major threat to stroke survivors with the problems of gait and balance disorders in the rehabilitation phase following severe consequences on quality of life and a heavy burden to their families. Many solutions have been proposed to assess fall risk for elders based on inertial sensor‐based signals, however, there still exists a great challenge of transferring them from elderly populations to the stroke‐survivors populations as gait disorder patterns are significant difference between elders and stroke survivors. In this study, we conduct a pilot study to collect inertial sensor‐based signals from stroke survivors when they performed the timed up and go test, and build an automatic fall risk assessment model with the architecture of Siamese network, with a merit of mitigating the problem of small sample size. Specifically, the proposed automatic fall risk assessment model consists of two parallel convolutional neural networks, each of which is composed of three convolutional layers, two max‐pooling layers, and three fully connected layers. To utilize the space relation among accelerator‐based and gyroscope‐based signals, two‐dimensional discrete wavelet transform extracts image‐like features, wavelet coefficients, from inertial sensor‐based signals as the input. Experimental results show that the proposed fall risk assessment model has achieved a promising results, which outperform cutting‐edge methods with a big margin. The proposed fall risk assessment model with low computational complexity and limited memory consuming can be deployed on an embedded system to provide fall risk assessment service for stroke survivors in point‐of‐care environments or community settings.

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