A novel human–machine interface based on recognition of multi-channel facial bioelectric signals
A novel human–machine interface based on recognition of multi-channel facial bioelectric signals
复制标题
一种基于多通道面部生物电信号识别的新型人机界面
DOI：
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发表时间：
2011
影响因子：
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通讯作者：
Reza Golpayegani
中科院分区：
文献类型：
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作者：
Iman Mohammad;Rezazadeh bullet;S. Mohammad;F. bullet;Huosheng Hu;bullet S Mohammad;Reza Golpayegani
本文提出了一种新颖的人机界面，供残疾人与辅助系统交互，以提高生活质量。它基于通过在额肌和颞肌面部肌肉上放置三对电极（物理通道）获取的多通道前额生物电信号。采集到的信号通过并行滤波器组，探索与面部肌电图、眼电图和脑电图相关的三个不同子带。提取在非重叠 256 ms 窗口内分析的生物电信号的均方根特征。应用减法模糊c均值聚类方法（SFCM）来分割特征空间并生成基于Takagi-Sugeno规则的初始模糊。然后，利用自适应神经模糊推理系统来调整提取的 SFCM 规则的前提和结果参数。根据逻辑特征的不同组合和融合，8种不同面部动作（微笑、皱眉、左/右唇角上拉、眼球向左/右/上/下运动）的平均分类器判别率在93.04%到96.99%之间。实验结果表明，所提出的接口对于 8 种基本面部手势的辨别具有高度的准确性和鲁棒性。还讨论了我们的人机界面方法的一些潜在和进一步的功能。
This paper presents a novel human–machine interface for disabled people to interact with assistive systems for a better quality of life. It is based on multi-channel forehead bioelectric signals acquired by placing three pairs of electrodes (physical channels) on the Frontalis and Temporalis facial muscles. The acquired signals are passed through a parallel filter bank to explore three different sub-bands related to facial electromyogram, electrooculogram and electroencephalogram. The root mean square features of the bioelectric signals analyzed within non-overlapping 256 ms windows were extracted. The subtractive fuzzy c-means clustering method (SFCM) was applied to segment the feature space and generate initial fuzzy based Takagi–Sugeno rules. Then, an adaptive neuro-fuzzy inference system is exploited to tune up the premises and consequence parameters of the extracted SFCMs rules. The average classifier discriminating ratio for eight different facial gestures (smiling, frowning, pulling up left/right lips corner, eye movement to left/right/up/down) is between 93.04% and 96.99% according to different combinations and fusions of logical features. Experimental results show that the proposed interface has a high degree of accuracy and robustness for discrimination of 8 fundamental facial gestures. Some potential and further capabilities of our approach in human–machine interfaces are also discussed.
影响因子：
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作者：
Armando Barreto;Scott Scargle;Malek Adjouadi
通讯作者：
Armando Barreto;Scott Scargle;Malek Adjouadi