Affective annotation refers to the process of labeling media content based on the emotions they evoke. Since such experiences are inherently subjective and depend on individual differences, the central challenge is associating digital content with its affective, interindividual experience. Here, we present a first-of-its-kind methodology for affective annotation directly from brain signals by monitoring the affective experience of a crowd of individuals via functional near-infrared spectroscopy (fNIRS). An experiment is reported in which fNIRS was recorded from 31 participants to develop a brain-computer interface (BCI) for affective annotation. Brain signals evoked by images were used to draw predictions about the affective dimensions that characterize the stimuli. By combining annotations, the results show that monitoring crowd responses can draw accurate affective annotations, with performance improving significantly with increases in crowd size. Our methodology demonstrates a proof-of-concept to source affective annotations from a crowd of BCI users without requiring any auxiliary mental or physical interaction.

中文翻译：

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通过 fNIRS-BCI 众包情感注释

情感注释是指根据媒体内容唤起的情感来标记媒体内容的过程。由于此类体验本质上是主观的并且取决于个体差异，因此核心挑战是将数字内容与其情感的个体间体验联系起来。在这里，我们提出了一种首创的方法，通过功能性近红外光谱（fNIRS）监测一群人的情感体验，直接从大脑信号进行情感注释。据报道，一项实验记录了 31 名参与者的 fNIRS，以开发用于情感注释的脑机接口 (BCI)。由图像引起的大脑信号被用来预测表征刺激的情感维度。通过结合注释，结果表明，监控人群反应可以得出准确的情感注释，并且随着人群规模的增加，性能显着提高。我们的方法展示了一种概念验证，可以从 BCI 用户群体中获取情感注释，而无需任何辅助的心理或身体交互。