Signal Quality Assessment Model for Wearable EEG Sensor on Prediction of Mental Stress
Signal Quality Assessment Model for Wearable EEG Sensor on Prediction of Mental Stress
复制标题
可穿戴脑电传感器预测精神压力的信号质量评估模型
DOI：
10.1109/tnb.2015.2420576
复制
发表时间：
2015-07-01
影响因子：
3.9
通讯作者：
Moore, Philip
中科院分区：
文献类型：
--
作者：
Hu, Bin;Peng, Hong;Moore, Philip
脑电图（EEG）在电子医疗系统中起着重要的作用，特别是在精神卫生领域，其中持续和不引人注目的监测是理想的。在OPTIMI项目的背景下，设计并制作了一种新型、低成本、重量轻的可穿戴式脑电传感器。为了提高EEG传感器在现实生活中的性能和可靠性，我们提出了一种方法来评估EEG信号的质量，基于该方法，用户可以轻松地调整电极与皮肤之间的连接。我们的方法有助于过滤无效的EEG数据从个人试验在国内和办公室设置。然后，我们应用离散小波变换（DWT）和自适应噪声消除（ANC）的基础上，已被设计为从EEG信号中去除眼伪影（OA）的算法。小波变换被应用于获得重建的OA信号作为参考，而ANC，基于递归最小二乘法，被用来从原始EEG数据中去除OA。新生产的传感器在OPTIMI框架内进行了测试和部署，用于慢性压力检测。脑电非线性动力学特征和额叶θ、α和β带的不对称性已被选为慢性应激的生物学指标，表明应激个体的右前脑电数据活动相对较大。评估结果表明，我们的EEG传感器和数据处理算法已经成功地解决了便携式系统的要求和挑战，病人监护，由欧盟OPTIMI项目的设想。
Electroencephalogram (EEG) plays an important role in E-healthcare systems, especially in the mental healthcare area, where constant and unobtrusive monitoring is desirable. In the context of OPTIMI project, a novel, low cost, and light weight wearable EEG sensor has been designed and produced. In order to improve the performance and reliability of EEG sensors in real-life settings, we propose a method to evaluate the quality of EEG signals, based on which users can easily adjust the connection between electrodes and their skin. Our method helps to filter invalid EEG data from personal trials in both domestic and office settings. We then apply an algorithm based on Discrete Wavelet Transformation (DWT) and Adaptive Noise Cancellation (ANC) which has been designed to remove ocular artifacts (OA) from the EEG signal. DWT is applied to obtain a reconstructed OA signal as a reference while ANC, based on recursive least squares, is used to remove the OA from the original EEG data. The newly produced sensors were tested and deployed within the OPTIMI framework for chronic stress detection. EEG nonlinear dynamics features and frontal asymmetry of theta, alpha, and beta bands have been selected as biological indicators for chronic stress, showing relative greater right anterior EEG data activity in stressful individuals. Evaluation results demonstrate that our EEG sensor and data processing algorithms have successfully addressed the requirements and challenges of a portable system for patient monitoring, as envisioned by the EU OPTIMI project.