基于脑机接口的智能环境控制

M. Singh, I. Saini, Neetu Sood
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引用次数: 0

摘要

脑机接口(BCI)系统的主要优点是它可以实现大脑和计算机之间的直接通信。提出了一种基于脑电图(EEG)的智能环境控制BCI系统。从EEG数据中提取ISRUC-Sleep特征。从数据中提取的特征用于训练分类器,用于对人的认知阶段(警觉、放松和睡眠)进行分类。在MATLAB上设计了基于加权k近邻(Wk-NN)算法的分类器。环境是根据人的认知状态来控制的。对认知状态的分类准确率为92.5%。最后进行了原型智能环境控制的实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain Computer Interface Based Smart Environment Control
The main advantage of a Brain Computer Interfaced (BCI) systems is that it enables direct communication between brain and computer. This study proposes an Electroencephalogram (EEG) based BCI system for smart environment control. Features from EEG data, ISRUC-Sleep was extracted. Extracted features from data were used for training a classifier for classification of the cognitive stage of the person (Alert, Relaxed and Sleep). The weighted k nearest neighbor (Wk-NN) algorithm based classifier was designed on MATLAB. And the environment was controlled based on the cognitive state of the person. The accuracy achieved for classification of the cognitive state was 92.5%. At last a prototype smart environment control was practiced.
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