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Content categorization based on implicit and explicit user feedback: Combining self-reports with EEG emotional state analysis

机译:基于隐式和显式用户反馈的内容分类:将自我报告与EEG情绪状态分析相结合

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We present a study that combines and compares explicit (questionnaire-generated) and implicit (EEG-based) feedback from test subjects on perceptual dimensions of different types of audiovisual content. We found significant differences in importance and evaluation of perceptual-, viewer-and clip-related dimensions across a limited set of contents. The results suggest that additional bio-feedback data can help to increase validity and robustness of user feedback in Quality of Experience (QoE) and content categorization research.
机译:我们提出了一项研究,该研究结合并比较了测试对象对不同类型的视听内容的感知维度的显式(问卷生成)和隐式(基于EEG)反馈。我们发现,在有限的一组内容中,感知,观看者和剪辑相关维度的重要性和评估存在重大差异。结果表明,其他生物反馈数据可以帮助提高体验质量(QoE)和内容分类研究中用户反馈的有效性和健壮性。

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