Bosch Paul, Herrera Mauricio, López Julio, Maldonado Sebastián
Facultad de Ingeníera, Universidad del Desarrollo, Av. Plaza 700, Las Condes, Santiago, Chile.
Facultad de Ingeniería y Ciencias, Universidad Diego Portales, Ejército 441, Santiago, Chile.
Behav Neurol. 2018 Jan 11;2018:4638903. doi: 10.1155/2018/4638903. eCollection 2018.
We have developed a new methodology for examining and extracting patterns from brain electric activity by using data mining and machine learning techniques. Data was collected from experiments focused on the study of cognitive processes that might evoke different specific strategies in the resolution of math problems. A binary classification problem was constructed using correlations and phase synchronization between different electroencephalographic channels as characteristics and, as labels or classes, the math performances of individuals participating in specially designed experiments. The proposed methodology is based on using well-established procedures of feature selection, which were used to determine a suitable brain functional network size related to math problem solving strategies and also to discover the most relevant links in this network without including noisy connections or excluding significant connections.
我们已经开发出一种新方法，通过使用数据挖掘和机器学习技术来检查和提取大脑电活动中的模式。数据是从专注于研究认知过程的实验中收集的，这些认知过程可能会在解决数学问题时引发不同的特定策略。利用不同脑电图通道之间的相关性和相位同步作为特征构建了一个二元分类问题，并将参与专门设计实验的个体的数学表现作为标签或类别。所提出的方法基于使用成熟的特征选择程序，这些程序用于确定与数学问题解决策略相关的合适大脑功能网络大小，还用于发现该网络中最相关的联系，而不包括噪声连接或排除重要连接。
