• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.2
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• pp.217-222
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• 2004

A person does communication between each other using language. But, In the case of disabled person, cannot communicate own idea to use writing and gesture. We embodied communication system using the EEG so that disabled person can do communication. After feature extraction of the EEG included facial muscle signals, it is converted the facial muscle into control signal, and then did so that can select character and communicate idea.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.289-292
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• 2009

A person does communication between each other using language. But In the case of disabled person can not communication own idea to use writing and gesture. Therefore, In this paper, we embodied communication system using the facial muscle signals so that disabled person can do communication. Especially, After feature extraction of the EEG included facial muscle, it is converted the facial muscle into control signal, and then select character and communicate using a minimum list keyboard.

• Speech Sciences
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• v.11 no.1
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• pp.55-64
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• 2004

This study was to determine the effects of a massage and oro-facial exercise program on spastic dysarthric patients' lip muscle function using an electromyogram (EMG). Three subjects with Spastic Dysarthria participated in the study. The surface electrodes were positioned on the Levator Labii Superior Muscle (LLSM), Depressor Labii Inferior Muscle (DLIM), and Orbicularis Oris Muscle (OOM). To examine lip muscle function improvement, the EMG signals were analyzed in terms of RMS (Root Mean Square) values and Median Frequency. In addition, the diadochokinetic movements and the rate of sentence reading were measured. The results revealed that the RMS values were decreased and the Median Frequency moved to a high frequency area. Diadochokinesis and sentence reading rates were improved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.6
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• pp.1338-1344
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• 2010

A person does communication between each other using language. But In the case of disabled person can not communication own idea to use writing and gesture. Therefore, In this paper, we embodied communication system using the facial muscle signals so that disabled person can do communication. Especially, After feature extraction of the EEG included facial muscle, it is converted the facial muscle into control signal, and then select character and communication using a minimum list keyboard.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.143-150
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• 2016

In this paper, we propose Korean monophthong recognition method optimizing muscle mixing based on facial surface EMG signals. We observed that EMG signal patterns and muscle activity may vary according to Korean monophthong pronunciation. We use RMS, VAR, MMAV1, MMAV2 which were shown high recognition accuracy in previous study and Cepstral Coefficients as feature extraction algorithm. And we classify Korean monophthong by QDA(Quadratic Discriminant Analysis) and HMM(Hidden Markov Model). Muscle mixing optimized using input data in training phase, optimized result is applied in recognition phase. Then New data are input, finally Korean monophthong are recognized. Experimental results show that the average recognition accuracy is 85.7% in QDA, 75.1% in HMM.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.427-432
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• 2003

EEG is an electrical signal, which occurs during information processing in the brain. These EEG signals have been used clinically, but nowadays we ate mainly studying Brain-Computer Interface (BCI) such as interfacing with a computer through the EEG, controlling the machine through the EEG. The ultimate purpose of BCI study is specifying the EEG at various mental states so as to control the computer and machine. This research makes the controlling system of directions with the artifact that are generated from the subject s will, for the purpose of controlling the machine correctly and reliably We made the system like this. First, we select the particular artifact among the EEG mixed with artifact, then, recognize and classify the signals pattern, then, change the signals to general signals that can be used by the controlling system of directions.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.11
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• pp.502-510
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• 2006

In this paper, we describe implementation of a computer access device for the severly motor-disability. Many people with severe motor disabilities need an augmentative communication technology. Those who are totally paralyzed, or 'locked-in' cannot use conventional augmentative technologies, all of which require some measure of muscle control. The forehead is often the last site to suffer degradation in cases of severe disability and degenerative disease. For example, In ALS(Amyotrophic Lateral Sclerosis) and MD(Muscular dystrophy) the ocular motorneurons and ocular muscles are usually spared permitting at least gross eye movements, but not precise eye pointing. We use brain and body forehead bio-potentials in a novel way to generate multiple signals for computer control inputs. A bio-amplifier within this device separates the forehead signal into three frequency channels. The lowest channel is responsive to bio-potentials resulting from an eye motion, and second channel is the band pass derived between 0.5 and 45Hz, falling within the accepted Electroencephalographic(EEG) range. A digital processing station subdivides this region into eleven components frequency bands using FFT algorithm. The third channel is defined as an Electromyographic(EMG) signal. It responds to contractions of facial muscles and is well suited to discrete on/off switch closures, keyboard commands. These signals are transmitted to a PC that analyzes in a time series and a frequency region and discriminates user's intentions. That software graphically displays user's bio-potential signals in the real time, therefore user can see their own bio-potentials and control their physiological signals little by little after some training sessions. As a result, we confirmed the performance and availability of the developed system with experimental user's bio-potentials.