Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:406-409. doi: 10.1109/EMBC48229.2022.9871388.
Affective brain-computer interfaces are a fast-growing area of research. Accurate estimation of emotional states from physiological signals is of great interest to the fields of psychology and human-computer interaction. The DEAP dataset is one of the most popular datasets for emotional classification. In this study we generated heat maps from spectral data within the neurological signals found in the DEAP dataset. To account for the class imbalance within this dataset, we then discarded images belonging to the larger class. We used these images to fine-tune several Big Transfer neural networks for binary classification of arousal, valence, and dominance affective states. Our best classifier was able to achieve greater than 98% accuracy and 990% balanced accuracy in all three classification tasks. We also investigated the effects of this balancing method on our classifiers.
情感脑机接口是一个快速发展的研究领域。从生理信号中准确估计情绪状态是心理学和人机交互领域非常感兴趣的问题。DEAP 数据集是情感分类最受欢迎的数据集之一。在这项研究中，我们从 DEAP 数据集中的神经信号的光谱数据中生成了热图。为了解决数据集内的类别不平衡问题，我们随后丢弃了属于较大类别的图像。我们使用这些图像来微调几个 Big Transfer 神经网络，以对唤醒度、愉悦度和主导度情感状态进行二进制分类。我们最好的分类器在所有三个分类任务中都能够实现超过 98%的准确率和 990%的平衡准确率。我们还研究了这种平衡方法对我们的分类器的影响。
