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Predicting Empathy From Resting State Brain Connectivity: A Multivariate Approach.
Recent task fMRI studies suggest that individual differences in trait empathy and empathic concern are mediated by patterns of connectivity between self-other resonance and top-down control networks that are stable across task demands. An untested implication of this hypothesis is that these stable patterns of connectivity should be visible even in the absence of empathy tasks. Using machine learning, we demonstrate that patterns of resting state fMRI connectivity (i.e. the degree of synchronous BOLD activity across multiple cortical areas in the absence of explicit task demands) of resonance and control networks predict trait empathic concern (n = 58). Empathic concern was also predicted by connectivity patterns within the somatomotor network. These findings further support the role of resonance-control network interactions and of somatomotor function in our vicariously driven concern for others. Furthermore, a practical implication of these results is that it is possible to assess empathic predispositions in individuals without needing to perform conventional empathy assessments
Modelling affect for horror soundscapes
The feeling of horror within movies or games relies on the audience’s perception of a tense atmosphere — often achieved
through sound accompanied by the on-screen drama — guiding its emotional experience throughout the scene or game-play
sequence. These progressions are often crafted through an a priori knowledge of how a scene or game-play sequence will playout, and
the intended emotional patterns a game director wants to transmit. The appropriate design of sound becomes even more challenging
once the scenery and the general context is autonomously generated by an algorithm. Towards realizing sound-based affective
interaction in games this paper explores the creation of computational models capable of ranking short audio pieces based on
crowdsourced annotations of tension, arousal and valence. Affect models are trained via preference learning on over a thousand
annotations with the use of support vector machines, whose inputs are low-level features extracted from the audio assets of a
comprehensive sound library. The models constructed in this work are able to predict the tension, arousal and valence elicited by
sound, respectively, with an accuracy of approximately 65%, 66% and 72%.peer-reviewe
Multimodal Content Analysis for Effective Advertisements on YouTube
The rapid advances in e-commerce and Web 2.0 technologies have greatly
increased the impact of commercial advertisements on the general public. As a
key enabling technology, a multitude of recommender systems exists which
analyzes user features and browsing patterns to recommend appealing
advertisements to users. In this work, we seek to study the characteristics or
attributes that characterize an effective advertisement and recommend a useful
set of features to aid the designing and production processes of commercial
advertisements. We analyze the temporal patterns from multimedia content of
advertisement videos including auditory, visual and textual components, and
study their individual roles and synergies in the success of an advertisement.
The objective of this work is then to measure the effectiveness of an
advertisement, and to recommend a useful set of features to advertisement
designers to make it more successful and approachable to users. Our proposed
framework employs the signal processing technique of cross modality feature
learning where data streams from different components are employed to train
separate neural network models and are then fused together to learn a shared
representation. Subsequently, a neural network model trained on this joint
feature embedding representation is utilized as a classifier to predict
advertisement effectiveness. We validate our approach using subjective ratings
from a dedicated user study, the sentiment strength of online viewer comments,
and a viewer opinion metric of the ratio of the Likes and Views received by
each advertisement from an online platform.Comment: 11 pages, 5 figures, ICDM 201
Machine Learning for Neuroimaging with Scikit-Learn
Statistical machine learning methods are increasingly used for neuroimaging
data analysis. Their main virtue is their ability to model high-dimensional
datasets, e.g. multivariate analysis of activation images or resting-state time
series. Supervised learning is typically used in decoding or encoding settings
to relate brain images to behavioral or clinical observations, while
unsupervised learning can uncover hidden structures in sets of images (e.g.
resting state functional MRI) or find sub-populations in large cohorts. By
considering different functional neuroimaging applications, we illustrate how
scikit-learn, a Python machine learning library, can be used to perform some
key analysis steps. Scikit-learn contains a very large set of statistical
learning algorithms, both supervised and unsupervised, and its application to
neuroimaging data provides a versatile tool to study the brain.Comment: Frontiers in neuroscience, Frontiers Research Foundation, 2013, pp.1
Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications
This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments
A Review on EEG Signals Based Emotion Recognition
Emotion recognition has become a very controversial issue in brain-computer interfaces (BCIs). Moreover, numerous studies have been conducted in order to recognize emotions. Also, there are several important definitions and theories about human emotions. In this paper we try to cover important topics related to the field of emotion recognition. We review several studies which are based on analyzing electroencephalogram (EEG) signals as a biological marker in emotion changes. Considering low cost, good time and spatial resolution, EEG has become very common and is widely used in most BCI applications and studies. First, we state some theories and basic definitions related to emotions. Then some important steps of an emotion recognition system like different kinds of biologic measurements (EEG, electrocardiogram [EEG], respiration rate, etc), offline vs online recognition methods, emotion stimulation types and common emotion models are described. Finally, the recent and most important studies are reviewed
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis
Multi-view graph embedding has become a widely studied problem in the area of
graph learning. Most of the existing works on multi-view graph embedding aim to
find a shared common node embedding across all the views of the graph by
combining the different views in a specific way. Hub detection, as another
essential topic in graph mining has also drawn extensive attentions in recent
years, especially in the context of brain network analysis. Both the graph
embedding and hub detection relate to the node clustering structure of graphs.
The multi-view graph embedding usually implies the node clustering structure of
the graph based on the multiple views, while the hubs are the boundary-spanning
nodes across different node clusters in the graph and thus may potentially
influence the clustering structure of the graph. However, none of the existing
works in multi-view graph embedding considered the hubs when learning the
multi-view embeddings. In this paper, we propose to incorporate the hub
detection task into the multi-view graph embedding framework so that the two
tasks could benefit each other. Specifically, we propose an auto-weighted
framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain
network analysis. The MVGE-HD framework learns a unified graph embedding across
all the views while reducing the potential influence of the hubs on blurring
the boundaries between node clusters in the graph, thus leading to a clear and
discriminative node clustering structure for the graph. We apply MVGE-HD on two
real multi-view brain network datasets (i.e., HIV and Bipolar). The
experimental results demonstrate the superior performance of the proposed
framework in brain network analysis for clinical investigation and application
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