Abstract：
Alcohol use is a leading risk factor for substantial health loss, disability, and death. Thus, there is a general interest in developing computational tools to classify electroencephalographic (EEG) signals in alcoholism, but there are a limited number of studies on convolutional neural network (CNN) classification of alcoholism using topographic EEG signals. We produced an original dataset recorded from Brazilian subjects performing a language recognition task. Then, we transformed the Event-Related Potentials (ERPs) into topographic maps by using the ERP\'s statistical parameters across time, and used a CNN network to classify the topographic dataset. We tested the effect of the size of the dataset in the accuracy of the CNNs and proposed a data augmentation approach to increase the size of the topographic dataset to improve the accuracies. Our results encourage the use of CNNs to classify abnormal topographic EEG patterns associated with alcohol abuse.
摘要：
饮酒是导致严重健康损失的主要风险因素，残疾,和死亡。因此，人们普遍对开发计算工具对酒精中毒中的脑电图（EEG）信号进行分类感兴趣，但是关于使用地形EEG信号对酒精中毒进行卷积神经网络（CNN）分类的研究数量有限。我们制作了从执行语言识别任务的巴西受试者记录的原始数据集。然后，我们通过使用ERP的时间统计参数将事件相关电位(ERP)转换为地形图，并使用CNN网络对地形数据集进行分类。我们测试了数据集大小对CNN准确性的影响，并提出了一种数据增强方法来增加地形数据集的大小以提高准确性。我们的结果鼓励使用CNN对与酒精滥用相关的异常地形脑电图模式进行分类。
