Visual short-term memory (VSTM) is defined as the ability to remember a small amount of visual information, such as colors and shapes, during a short period of time. VSTM is a part of short-term memory, which can hold information up to 30 seconds. In this paper, we present the results of research where we classified the data gathered by using an electroencephalogram (EEG) during a VSTM experiment. The experiment was performed with 12 participants that were required to remember as many details as possible from the two images, displayed for 1 minute. The first assessment was done in an isolated environment, while the second assessment was done in front of the other participants, in order to increase the stress of the examinee. The classification of the EEG data was done by using four algorithms: Naive Bayes, support vector, KNN, and random forest. The results obtained show that AI-based classification could be successfully used in the proposed way, since we were able to correctly classify the order of the images presented 90.12% of the time and type of the displayed image 90.51% of the time.

中文翻译：

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使用基于AI的分类技术处理视觉短期记忆评估期间收集的EEG数据

视觉短期记忆（VSTM）定义为能够在短时间内记住少量视觉信息（例如颜色和形状）的能力。VSTM是短期记忆的一部分，可以存储长达30秒的信息。在本文中，我们介绍了研究结果，我们在VSTM实验中使用脑电图（EEG）对收集的数据进行了分类。实验是由12位参与者进行的，要求他们从显示1分钟的两个图像中记住尽可能多的细节。第一次评估是在一个孤立的环境中进行的，而第二次评估是在其他参与者面前进行的，以增加考生的压力。EEG数据的分类通过以下四种算法完成：朴素贝叶斯，支持向量，KNN和随机森林。