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首页> 外文会议>2017 International Conference on Inventive Computing and Informatics >Home automation control system implementation using SSVEP based brain computer interface
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Home automation control system implementation using SSVEP based brain computer interface

机译：使用基于SSVEP的脑计算机接口实现家庭自动化控制系统

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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：这项研究提出了用于家用电器控制系统的脑电图的视觉诱发电位（EEG）信号，以促进患有严重运动障碍的残障人士对家用电器的集成和控制。脑机接口（BCI）充当大脑与计算机之间的有利通信接口，使神经信号能够控制外围设备。已经实现了基于稳态视觉诱发电位（SSVEP）控制方式的非侵入式思维机接口，并已用于在离线环境中控制家用电器。通过将电极置于人脑的视觉皮层区域，已从10位受试者中获得了基于SSVEP的EEG信号。受试者通过将视线聚焦在SSVEP盒上具有10、11、12和13 Hz不同频率的四个闪烁LED之一上来控制所需的设备。已使用最小能量确定了获取的脑电数据的特征。特征向量的分类已通过线性判别分析（LDA）完成。预期的控制命令被发送到Arduino，以控制家用电器。来自10个主题的数据已用于训练已在验证过程中使用的分类器模型。分类器模型的总体准确性已发现为84.8％。使用两个对象的数据进行数据验证的准确性分别表示为80.6％和81.8％。

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