The electroencephalographic activity of particular brain areas during the decision making process is still little known. This paper presents results of experiments on the group of 30 patients with a wide range of psychiatric disorders and 41 members of the control group. All subjects were performing the Iowa Gambling Task that is often used for decision process investigations. The electroencephalographical activity of participants was recorded using the dense array amplifier. The most frequently active Brodmann Areas were estimated by means of the photogrammetry techniques and source localization algorithms. The analysis was conducted in the full frequency as well as in alpha, beta, gamma, delta, and theta bands. Next the mean electric charge flowing through each of the most frequently active areas and for each frequency band was calculated. The comparison of the results obtained for the subjects and the control groups is presented. The difference in activity of the selected Brodmann Areas can be observed in all variants of the task. The hyperactivity of amygdala is found in both the patients and the control group. It is noted that the somatosensory association cortex, dorsolateral prefrontal cortex, and primary visual cortex play an important role in the decision-making process as well. Some of our results confirm the previous findings in the fMRI experiments. In addition, the results of the electroencephalographic analysis in the broadband as well as in specific frequency bands were used as inputs to several machine learning classifiers built in Azure Machine Learning environment. Comparison of classifiers' efficiency is presented to some extent and finding the most effective classifier may be important for planning research strategy toward finding decision-making biomarkers in cortical activity for both healthy people and those suffering from psychiatric disorders.

中文翻译：

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使用定量脑电图方法和机器学习工具分析决策过程

在决策过程中特定大脑区域的脑电图活动仍然鲜为人知。本文介绍了对 30 名患有多种精神疾病的患者和 41 名对照组成员的实验结果。所有受试者都在执行爱荷华赌博任务，该任务通常用于决策过程调查。使用密集阵列放大器记录参与者的脑电活动。通过摄影测量技术和源定位算法估计最频繁活跃的布罗德曼区域。分析是在全频以及 alpha、beta、gamma、delta 和 theta 波段进行的。接下来，计算流经每个最频繁活动区域和每个频带的平均电荷。呈现了对受试者和对照组获得的结果的比较。在任务的所有变体中都可以观察到所选布罗德曼区域活动的差异。在患者和对照组中均发现杏仁核过度活跃。值得注意的是，躯体感觉关联皮层、背外侧前额叶皮层和初级视觉皮层也在决策过程中发挥重要作用。我们的一些结果证实了先前在 fMRI 实验中的发现。此外，宽带以及特定频段的脑电图分析结果被用作 Azure 机器学习环境中构建的多个机器学习分类器的输入。分类器的比较