Convolutional Neural Network Classification of Topographic Electroencephalographic Maps on Alcoholism.

IF 6.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Victor Borghi Gimenez, Suelen Lorenzato Dos Reis, Fábio M Simões de Souza
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引用次数: 0

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.

酒精中毒脑电地形图的卷积神经网络分类。
饮酒是造成严重健康损失、残疾和死亡的主要危险因素。因此,人们普遍对开发计算工具来对酒精中毒的脑电图(EEG)信号进行分类感兴趣,但使用地形脑电图信号进行卷积神经网络(CNN)酒精中毒分类的研究数量有限。我们制作了一个原始数据集,记录了巴西受试者执行语言识别任务的情况。然后,利用事件相关电位(event - correlation potential, ERP)的统计参数,将事件相关电位(event - correlation potential, ERP)随时间的变化转化为地形图,并利用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精度的影响,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精度。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。
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来源期刊
International Journal of Neural Systems
International Journal of Neural Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
28.80%
发文量
116
审稿时长
24 months
期刊介绍: The International Journal of Neural Systems is a monthly, rigorously peer-reviewed transdisciplinary journal focusing on information processing in both natural and artificial neural systems. Special interests include machine learning, computational neuroscience and neurology. The journal prioritizes innovative, high-impact articles spanning multiple fields, including neurosciences and computer science and engineering. It adopts an open-minded approach to this multidisciplinary field, serving as a platform for novel ideas and enhanced understanding of collective and cooperative phenomena in computationally capable systems.
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