Emotion recognition based on electroencephalography (EEG) has received attention as a way to implement human-centric services. However, there is still much room for improvement, particularly in terms of the accuracy. In this paper, we propose novel deep learning approach using convolutional neural networks (CNNs) EEG-based emotion recognition. particular, employ brain connectivity features that have not been used with models previous studies, which can account synchronous activations...

This paper proposes a novel graph signal-based deep learning method for electroencephalography (EEG) and its application to EEG-based video identification. We present new methods effectively represent EEG data as signals on graphs, learn them using convolutional neural networks. Experimental results identification responses obtained while watching videos show the effectiveness of proposed approach in comparison existing methods. Effective schemes signal representation are also discussed.

Despite the remarkable progress in development of predictive models for healthcare, applying these algorithms on a large scale has been challenging. Algorithms trained particular task, based specific data formats available set medical records, tend to not generalize well other tasks or databases which fields may differ. To address this challenge, we propose General Healthcare Predictive Framework (GenHPF), is applicable any EHR with minimal preprocessing multiple prediction tasks. GenHPF...

High dynamic range (HDR) imaging has been attracting much attention as a technology that can provide immersive experience. Its ultimate goal is to better quality of experience (QoE) via enhanced contrast. In this paper, we analyze perceptual tone-mapped HDR videos both explicitly by conducting subjective questionnaire assessment and implicitly using EEG peripheral physiological signals. From the results assessment, it revealed are more interesting natural, give than low (LDR) videos....

In this paper, we deal with the issue of implicit monitoring perceptual responses to product design through electroencephalography (EEG) and eye tracking. Four evaluation factors, namely, preference, luxury, complexity, harmony are considered investigate how people perceive car design. particular, quantified predicted based on EEG gaze data. Average root-mean-square errors 0.210 1.215 obtained from subject-dependent subject-independent regressions a 7-point score scale, respectively, which...

Electroencephalography (EEG) is a useful way to implicitly monitor the user's perceptual state during multimedia consumption. One of primary challenges for practical use EEG-based monitoring achieve satisfactory level accuracy in EEG classification. Connectivity between different brain regions an important property classification EEG. However, how define connectivity structure given task still open problem, because there no ground truth about should be order maximize performance. In this...

High dynamic range (HDR) imaging has attracted attention as a new technology for immersive multimedia experience. In comparison to conventional low (LDR) contents, HDR contents are expected provide better quality of experience (QoE). this paper, we investigate implicit QoE measurement tone-mapped videos by using connectivity-based EEG features that convey information about simultaneous activations different brain regions and thus can explain the cognitive process than single channel powers....

We introduce the concept of "peripersonal space" an avatar in 3D virtual reality and discuss how it plays important role on navigation with different perspectives. By analyzing eye-gaze data avatar-based first-person perspective third-person perspective, we examine effects avatar's peripersonal space users' perceptual scopes within environments. propose that manipulating various perspectives has immediate perception as well patterns attentional capture. This study provides a helpful...

Many studies examining user responses to the product design have been implemented using photos instead of real products due practical limitations. In this study, we investigate validity such an approach for a particular case with car evaluation. We compare users' perceptual and cars. particular, employ both explicit implicit response channels, i.e., subjective rating, electroencephalography (EEG), visual attention. The results show that, although largely similar are obtained in two cases,...

This paper explores the relations between perspectives of navigation and visual perception in 3D virtual space, by analyzing avatar-based with eye-gaze data. We examine how different types avatars affect users' scopes within environments. Throughout this research, we attempt to draw possible connections cognitive patterns perception. propose that manipulating or those users has immediate effects on attention.

Evaluation of quality experience (Qo E) based on electroencephalography (EEG) has received great attention due to its capability real-time Qo E monitoring users. However, it still suffers from rather low recognition accuracy. In this paper, we propose a novel method using deep neural networks toward improved modeling EEG and thereby particular, aim model spatio-temporal characteristics relevant for QoE analysis within learning models. The results demonstrate the effectiveness proposed method.

High dynamic range (HDR) imaging has potential for providing immersive experience of multimedia contents. HDR contents are expected to have better perceptual quality than conventional low (LDR) contents, but the difference in brain between and LDR not been adequately studied. In this paper, we investigate tone-mapped videos based on electroencephalography (EEG) classification. A support vector machine (SVM) classification system is constructed using acquired EEG signals explore implicitly...

The cross-subject variability, or individuality, of electroencephalography (EEG) signals often has been an obstacle to extracting target-related information from EEG for classification subjects' perceptual states. In this paper, we propose a deep learning-based approach, which learns feature space mapping and performs individuality detachment reduce subject-related maximize performance. Our experiment on EEG-based video shows that our method significantly improves the accuracy.

In this paper we explore the relations between perspectives of navigation and electroencephalogram (EEG) in 3D virtual space. We analyze three types with EEG recordings examine how affect users' electrical activities their brains. Via a small-scale experiment, find that influence peripersonal space is altered by perspective, it can be observed via monitoring. These results have interesting implications on reality applications where sense agency, or task takes important roles.

Electroencephalography (EEG) has attracted much attention because it allows to monitor user states in real time and is applicable applications for improvement of multimedia experience. The repeatability EEG-based perceptual response analysis critical the reliability such applications, which not been sufficiently addressed previous studies. In this paper, we evaluate image quality assessment. We repeatedly perform same experiment three successive days. Then, design a classification system...

Emotion recognition based on electroencephalography (EEG) has received attention as a way to implement human-centric services. However, there is still much room for improvement, particularly in terms of the accuracy. In this paper, we propose novel deep learning approach using convolutional neural networks (CNNs) EEG-based emotion recognition. particular, employ brain connectivity features that have not been used with models previous studies, which can account synchronous activations...

Despite the abundance of Electronic Healthcare Records (EHR), its heterogeneity restricts utilization medical data in building predictive models. To address this challenge, we propose Universal Predictive Framework (UniHPF), which requires no domain knowledge and minimal pre-processing for multiple prediction tasks. Experimental results demonstrate that UniHPF is capable large-scale EHR models can process any form from distinct systems. We believe our findings provide helpful insights...