Locating cognitive task states by measuring changes in electrocortical activity due to various attentional and sensory-motor changes, has been in research interest since last few decades. In this paper, different cognitive states while performing various attentional and visuo-motor coordination tasks, are classified using electroencephalogram (EEG) signal. A non-linear time-series method, multifractal detrended fluctuation analysis (MFDFA) , is applied on respective EEG signal for features. Using MFDFA based features a multinomial classification is achieved. Nine channel EEG signal was recorded for 38 young volunteers (age: \(25\pm 5\) years, 30 male and 8 female), during six consecutive tasks. First three tasks are related to increasing levels of selective focus vision; next three are reflex and response based computer tasks. Total of 90 features (ten features from each of nine channel) were extracted from Hurst and singularity exponents of MFDFA on EEG signals. After feature selection, a multinomial classifier of six classes using two methods: support vector machine (SVM) and decision tree classifier (DTC). An accuracy of 96.84% using SVM and 92.49% using DTC was achieved.

通过测量由于各种注意和感觉运动变化引起的电皮层活动的变化来定位认知任务状态，自过去几十年以来一直受到研究兴趣。在本文中，使用脑电图 (EEG) 信号对执行各种注意力和视觉运动协调任务时的不同认知状态进行分类。非线性时间序列方法，多重分形去趋势波动分析（MFDFA），应用于相应的脑电图信号的特征。使用基于 MFDFA 的特征可以实现多项式分类。记录了 38 名年轻志愿者（年龄：\(25\pm 5\)年，30 名男性和 8 名女性），在连续六次任务中。前三项任务与提高选择性聚焦视觉水平有关；接下来的三个是基于反射和反应的计算机任务。从 EEG 信号的 MFDFA 的 Hurst 和奇点指数中提取了总共 90 个特征（九个通道中的每个通道有十个特征）。特征选择后，一个六类多项式分类器使用两种方法：支持向量机（SVM）和决策树分类器（DTC）。使用 SVM 的准确率为 96.84%，使用 DTC 的准确率为 92.49%。