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A novel human–machine interface based on recognition of multi-channel facial bioelectric signals

Mohammad Rezazadeh, Iman and Firoozabadi, S Mohammad and Hu, Huosheng and Hashemi Golpayegani, S Mohammad Reza (2011) 'A novel human–machine interface based on recognition of multi-channel facial bioelectric signals.' Australasian Physical & Engineering Sciences in Medicine, 34 (4). pp. 497-513. ISSN 0158-9938

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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. © 2011 Australasian College of Physical Scientists and Engineers in Medicine.

Item Type: Article
Uncontrolled Keywords: Human-machine interfaces; Multi-channel facial bioelectric signals; Subtractive fuzzy c-means clustering; Adaptive neuro-fuzzy inference system (ANFIS)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 15 Jul 2015 15:51
Last Modified: 23 Sep 2022 18:45

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