基于眼电图的数字识别设计语言障碍患者被动交流系统

Arnab Rakshit, A. Banerjee, D. Tibarewala
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引用次数: 10

摘要

HCI(人机接口)技术现在能够为遭受严重脑中风或脊髓损伤的语言残疾人士提供另一种支持。在所有的生物电位信号中,眼电信号具有表达日常生活中最需要的各种特征的能力。本文旨在提供一种新的康复HCI方法,成功地对受试者眼球运动绘制的数字进行分类,为了实现这一结果,设计了双通道眼电传感器和放大器,能够提取眼球运动引起的角膜视网膜电位的急剧变化,以绘制图案(数字、字母)。对提取的信号进行了成功的分类处理,准确率达到90%以上,精度和灵敏度值都比较合适。本文采用功率谱密度作为特征提取器,采用多层感知机核函数支持向量机作为特征分类器。其他分类器的性能也在这里进行了比较。选取12名健康受试者,采集眼球运动信号,用于识别与外界交流时频繁使用的不同数字。
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Electro-oculogram based digit recognition to design assitive communication system for speech disabled patients
HCI (Human Computer Interfacing) technology is now able to provide an alternative support to the speech disabled person who have undergone severe brain stroke or spinal cord injury. It has been presented here that amongst all bio potential signal Electro-Oculogram (EOG) signal has got the ability to represent all daily life characters which is most needed for communication. This paper is aimed to provide novel approach of rehabilitative HCI where it successfully classifies the numerical digits drawn by subject's eye movement and for achieving the result, electro-oculography sensors (dual channel) and amplifier has been designed, which is able to extract the sharp change of corneo retinal potential due to eyeball movement intended to draw a pattern (numeric digit, alphabet). The extracted signal has been processed and classified successfully with more than 90% accuracy rate and with suitable precision and sensitivity value. Here Power spectral density has been used as feature extractor and support vector machine with multilayer perceptron kernel function has been used as feature classifier. Performance of other classifiers also have been compared here. 12 healthy subjects took part in experiment and their eyeball movement signal has been acquired for distinguishing different numerical digits that are frequently needed for communication to external world.
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