An electroencephalogram (EEG) is a test that records electrical activity of the brain using electrodes attached to the scalp, and it has recently been used in conjunction with BMI (Brain-Machine Interface). Currently, the analysis of the EEG is visual, using graphic tools such as topographic maps. However, this analysis can be very difficult, so in this work, we apply a methodology of EEG analysis through data mining to analyze two different band frequencies of the brain signals (full band and Beta band) during an experiment where visually impaired and sighted individuals recognize spatial objects through the sense of touch. In this paper, we present details of the proposed methodology and a case study using decision trees to analyze EEG signals from visually impaired and sighted individuals during the execution of a spatial ability activity. In our experiment, the hypothesis was that sighted individuals, even if they are blindfolded, use vision to identify objects and that visually impaired people use the sense of touch to identify the same objects.

中文翻译：

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使用决策树分析EEG信号：幅度调制的研究。

脑电图（EEG）是一项使用附着在头皮上的电极记录大脑电活动的测试，最近已与BMI（脑机接口）结合使用。当前，使用诸如地形图的图形工具对脑电图进行分析是可视的。但是，这种分析可能非常困难，因此在这项工作中，我们通过数据挖掘应用脑电图分析的方法，以在有视力障碍和视力障碍的人的实验过程中分析两种不同的大脑信号频带（全频带和Beta频带）通过触觉识别空间物体。在本文中，我们介绍了拟议方法的详细信息，以及使用决策树分析空间能力活动执行过程中视力障碍者和视力障碍者的脑电信号的案例研究。