Department of Computer Engineering, CITIC Research Center & University of A Coruña, Campus de Elviña, A Coruña 15071, Spain.
Int J Neural Syst. 2020 Jul;30(7):2050018. doi: 10.1142/S0129065720500185. Epub 2020 May 4.
In this work, we develop open source hardware and software for eye state classification and integrate it with a protocol for the Internet of Things (IoT). We design and build the hardware using a reduced number of components and with a very low-cost. Moreover, we propose a method for the detection of open eyes (oE) and closed eyes (cE) states based on computing a power ratio between different frequency bands of the acquired signal. We compare several real- and complex-valued transformations combined with two decision strategies: a threshold-based method and a linear discriminant analysis. Simulation results show both classifier accuracies and their corresponding system delays.
在这项工作中，我们开发了用于眼部状态分类的开源硬件和软件，并将其与物联网 (IoT) 协议集成。我们使用较少的组件和非常低的成本来设计和构建硬件。此外，我们提出了一种基于计算所获取信号不同频带之间的功率比来检测睁眼 (oE) 和闭眼 (cE) 状态的方法。我们比较了几种实数和复数变换，并结合两种决策策略：基于阈值的方法和线性判别分析。仿真结果显示了分类器的准确性及其相应的系统延迟。
