• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Inventive Computing and Informatics >Home Automation Control System Implementation using SSVEP based Brain Computer Interface
【24h】

Home Automation Control System Implementation using SSVEP based Brain Computer Interface

机译：家庭自动化控制系统实现使用SSVEP基础的大脑电脑界面

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This study postulates Visual Evoked Potential of Electroencephalogram (EEG) signal for home appliance control system to facilitate the integration and control of the household electrical appliances by the handicapped persons suffering from severe motor disabilities. Brain-computer interface (BCI) acts as a favorable communication interface between the mind and computer empowering neural signals to control peripheral devices. Non-Invasive Mind Machine Interface based on the Steady State Visual Evoked Potential (SSVEP) control modality has been implemented and utilized in controlling the home appliances in an offline environment. SSVEP based EEG signal has been acquired from 10 subjects by placing the electrodes in the visual cortex region of human brain. The subject controls the desired appliance by focusing gaze on one of the four flickering LEDs on the SSVEP box having distinct frequencies of 10, 11, 12 and 13 Hz. The features of acquired EEG data have been determined using Minimum Energy. The classification of feature vector has been done by Linear Discriminant Analysis (LDA). The intended control commands are sent to the Arduino for controlling the home appliances. The data from 10 subjects has been used to train the classifier model which has been used in the validation process. The overall accuracy of classifier model has been found to be 84.8%. The data validation accuracy, using data of two subjects, has been indicated as 80.6% and 81.8% respectively.
机译：本研究假设家电控制系统的脑电图（EEG）信号的视觉诱发电位（EEG）信号，以促进患有严重运动残疾的残障人员的家用电器的整合和控制。脑电脑界面（BCI）作为智能和计算机之间的有利通信接口，从而使神经信号能够控制外围设备。基于稳态视觉诱发电位（SSVEP）控制模型的非侵入性思维机接口已经实现并利用在离线环境中控制家电。通过将电极放置在人脑的视觉皮质区域中，从10个受试者获得了SSVEP基EEG信号。该主题通过在具有10,11,12和13Hz的不同频率的SSVEP箱上的四个闪烁LED中聚焦凝视来控制所需的设备。采用EEG数据的特征已经使用最小能量确定。通过线性判别分析（LDA）完成了特征向量的分类。预期的控制命令被发送到Arduino以控制家用电器。来自10个受试者的数据已用于训练已在验证过程中使用的分类器模型。已发现分类器模型的整体准确性为84.8％。使用两个受试者的数据的数据验证精度分别表示为80.6％和81.8％。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号