Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:410-413. doi: 10.1109/EMBC48229.2022.9871624.
Affective states play an important role in human behavior and decision-making. In recent years, several affective brain-computer interface (aBCI) studies have focused on developing an emotion classifier based on elicited emotions within the user. However, it is difficult to achieve consistency in elicited emotions across populations, which can lead to dataset imbalances. The experimental design presented in this paper seeks to avoid consistency issues by asking the participant to classify the emotion portrayed in images of facial expressions, rather than their own emotions. Priming is also a common technique used in psychology studies that is known to influence emotional perception. To improve participant accuracy, we investigated matching and mis-matched word priming for the facial expression images. Electro-encephalogram (EEG) data were used to generate images fed into a classifier based on the Big Transfer model, BiT-M R101x1. The primed images resulted in higher classification accuracy overall. Further, by building different classifier models for both mis-matched primed images and matching primed images, we were able to achieve classification accuracies above 90%. We also provided the classifier with the true labels of the photographs instead of the labels generated by the participants and achieved similar results. The experimental paradigm of measuring brain activity during the emotional classification of another individual provides consistently high, balanced classification accuracies.
情感状态在人类行为和决策中起着重要作用。近年来，几项情感脑机接口 (aBCI) 研究集中于开发基于用户诱发情感的情绪分类器。然而，在不同人群中实现诱发情感的一致性是困难的，这可能导致数据集不平衡。本文提出的实验设计通过要求参与者对表情图像中的情绪进行分类，而不是对自己的情绪进行分类，从而避免了一致性问题。启动也是心理学研究中常用的一种技术，已知它会影响情绪感知。为了提高参与者的准确性，我们研究了表情图像的匹配和不匹配词启动。使用脑电图 (EEG) 数据生成图像，并基于大型迁移模型（BiT-M R101x1）将其输入到分类器中。启动后的图像总体上具有更高的分类准确性。此外，通过为不匹配的启动图像和匹配的启动图像构建不同的分类器模型，我们能够实现超过 90%的分类准确性。我们还为分类器提供了照片的真实标签，而不是参与者生成的标签，并且得到了类似的结果。在对另一个人的情绪进行分类时测量大脑活动的实验范式提供了一致的、高的、平衡的分类准确性。
