School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Sensors (Basel). 2023 Jan 9;23(2):761. doi: 10.3390/s23020761.
Attention refers to the human psychological ability to focus on doing an activity. The attention assessment plays an important role in diagnosing attention deficit hyperactivity disorder (ADHD). In this paper, the attention assessment is performed via a classification approach. First, the single-channel electroencephalograms (EEGs) are acquired from various participants when they perform various activities. Then, fast Fourier transform (FFT) is applied to the acquired EEGs, and the high-frequency components are discarded for performing denoising. Next, empirical mode decomposition (EMD) is applied to remove the underlying trend of the signals. In order to extract more features, singular spectrum analysis (SSA) is employed to increase the total number of the components. Finally, some typical models such as the random forest-based classifier, the support vector machine (SVM)-based classifier, and the back-propagation (BP) neural network-based classifier are used for performing the classifications. Here, the percentages of the classification accuracies are employed as the attention scores. The computer numerical simulation results show that our proposed method yields a higher classification performance compared to the traditional methods without performing the EMD and SSA.
注意是人类专注于某项活动的心理能力。注意力评估在诊断注意力缺陷多动障碍（ADHD）方面起着重要作用。在本文中，通过分类方法进行注意力评估。首先，当参与者进行各种活动时，从他们那里获取单通道脑电图（EEG）。然后，对获取的 EEG 应用快速傅里叶变换（FFT），并丢弃高频分量以进行去噪。接下来，应用经验模态分解（EMD）去除信号的潜在趋势。为了提取更多特征，采用奇异谱分析（SSA）增加组件总数。最后，使用一些典型的模型，如基于随机森林的分类器、基于支持向量机（SVM）的分类器和基于反向传播（BP）神经网络的分类器进行分类。这里，分类准确率的百分比用作注意力得分。计算机数值模拟结果表明，与不进行 EMD 和 SSA 的传统方法相比，我们提出的方法具有更高的分类性能。
