Hybrid video emotional tagging using users’ EEG and video content

Wang, Shangfei; Zhu, Yachen; Wu, Guobing; Ji, Qiang

doi:10.1007/s11042-013-1450-8

Cited by 51 publications

(37 citation statements)

References 75 publications

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“…Electrodes: When considering the number of electrodes, time interval required to set up an EEG device, comfort level of subjects, system usability, and number of features to be processed, it is advised, from this standpoint, that fewer electrodes be utilized; for example, five channels were used in [176,190]. Nonetheless, most current EEG devices still require a relatively large number of electrodes; for example, 64 channels were used in [93,95], and 32 channels were used in [43,71,191]. We found that the maximum number of electrodes that were used when recording EEG signals was 257 in [141], whereas the minimum number was one in [192].…”

Section: Design Innovation (Experimental) Papermentioning

confidence: 99%

“…Moreover, the subjects' emotions were recognized as displayed by EEG signals. These signals were then used to tag multimedia data [71,101,193,277]. These studies also investigated methods for implicit tagging, wherein users' responses to interactions with the multimedia content are analyzed to generate descriptive tags [71].…”

Section: Domain Description Referencesmentioning

confidence: 99%

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Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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Section: Design Innovation (Experimental) Papermentioning

confidence: 99%

Section: Domain Description Referencesmentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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“…However, few researchers have fully explored the relationships among the three [6]. Soleymani et al [42] analyzed the relationship between subjects' physiological response and video's emotional tag in both arousal and valence, as well as the relationship between the content of videos and the emotional tag in arousal and valence.…”

Section: Video Emotional Taggingmentioning

confidence: 99%

“…Soleymani et al [42] analyzed the relationship between subjects' physiological response and video's emotional tag in both arousal and valence, as well as the relationship between the content of videos and the emotional tag in arousal and valence. Wang et al [6] proposed hybrid approaches to annotate videos in valence and arousal space by using both users' EEG signals and video content. While the induced physiological signals are available during training, it is not convenient to collect users' physiological signals during application of emotion tagging.…”

Section: Video Emotional Taggingmentioning

confidence: 99%

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Emotion Recognition with the Help of Privileged Information

Wang

Zhu

Yue

et al. 2015

IEEE Trans. Auton. Mental Dev.

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In this paper, we propose a novel approach to recognize emotions with the help of privileged information, which is only available during training, but not available during testing. Such additional information can be exploited during training to construct a better classifier. Specifically, we recognize audience's emotion from EEG signals with the help of the stimulus videos, and tag videos' emotions with the aid of EEG signals. Firstly, frequency features are extracted from EEG signals and audio/visual features are extracted from video stimulus. Secondly, features are selected by statistical tests. Thirdly, a new EEG feature space and a new video feature space are constructed simultaneously using Canonical Correlation Analysis. Finally, two Support Vector Machines (SVM) are trained on the new EEG and video feature spaces respectively. During emotion recognition from EEG, only EEG signals are available, and the SVM classifier obtained on EEG feature space is used; while for video emotion tagging, only video clips are available, and the SVM classifier constructed on video feature space is adopted. Experiments of EEG-based emotion recognition and emotion video tagging are conducted on three benchmark databases, demonstrating that video content, as the context, can improve the emotion recognition from EEG signals and EEG signals available during training can enhance emotion video tagging.

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Emotion

Catrambone¹,

Valenza²

2021

Functional Brain-Heart Interplay

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Hybrid video emotional tagging using users’ EEG and video content

Cited by 51 publications

References 75 publications

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Emotion Recognition with the Help of Privileged Information

Emotion

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