• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.10
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• pp.831-841
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• 1991

A combined model of probabilistic and MLP(multi layer perceptron) model is proposed for the pattern classification of EMG( electromyogram) signals. The MLP model has a problem of not guaranteeing the global minima of error and different quality of approximations to Bayesian probabilities. The probabilistic model is, however, closely related to the estimation error of model parameters and the fidelity of assumptions. A proper combination of these will reduce the effects of the problems and be robust to input variations. Proposed model is able to get the MAP(maximum a posteriori probability) in the probabilistic model by estimating a priori probability distribution using the MLP model adaptively. This method minimize the error probability of the probabilistic model as long as the realization of the MLP model is optimal, and this is a good combination of the probabilistic model and the MLP model for the usage of MLP model reliability. Simulation results show the benefit of the proposed model compared to use the Mlp and the probabilistic model seperately and the average calculation time fro classification is about 50ms in the case of combined motion using an IBM PC 25 MHz 386model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.757-762
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• 2012

In this paper, the multi feature extraction algorithm for estimation of wrist movements based on Electromyogram(EMG) is proposed. For the extraction of precise features from the EMG signals, the difference absolute mean value(DAMV), the mean absolute value(MAV), the root mean square(RMS) and the difference absolute standard deviation value(DASDV) to consider amplitude characteristic of EMG signals are used. We figure out a more accurate feature-set by combination of two features out of these, because of multi feature extraction algorithm is more precise than single feature method. Also, for the motion classification based on EMG, the linear discriminant analysis(LDA), the quadratic discriminant analysis(QDA) and k-nearest neighbor(k-NN) are used. We implemented a test targeting twenty adult male to identify the accuracy of EMG pattern classification of wrist movements such as up, down, right, left and rest. As a result of our study, the LDA, QDA and k-NN classification method using feature-set with MAV and DASDV showed respectively 87.59%, 89.06%, 91.75% accuracy.

• Journal of Biomedical Engineering Research
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• v.27 no.5
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• pp.237-244
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• 2006

This study proposes an approach to the performance improvement of EMG(Electromyogram) pattern recognition. MFCC(Mel-Frequency Cepstral Coefficients)'s approach is molded after the characteristics of the human hearing organ. While it supplies the most typical feature in frequency domain, it should be reorganized to detect the features in EMG signal. And the dynamic aspects of EMG are important for a task, such as a continuous prosthetic control or various time length EMG signal recognition, which have not been successfully mastered by the most approaches. Thus, this paper proposes reorganized MFCC and HMM-GMM, which is adaptable for the dynamic features of the signal. Moreover, it requires an analysis on the most suitable system setting fur EMG pattern recognition. To meet the requirement, this study balanced the recognition-rate against the error-rates produced by the various settings when loaming based on the EMG data for each motion.

• Proceedings of the KOSOMBE Conference
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• v.1993 no.11
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• pp.145-150
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• 1993

It is an important component of the diagnosis to research the morphological changes of EMG in pathological conditions. In order to provide an EMG signal resulting from a predetermined neuromuscular pathophysiology, we have initially developed a mathmatical model of electromyographic interference pattern(IP). It can be used to study the variation of the IP resulting from morphological and electrophysiological changes occurring in disease states, because the model computes the IP from the underlying fiber and muscle structure. We performed quantative analysis or the model output, focusing on IPs resulting from simulations of dystrophic fiber loss and the MU denervation and reinnervation typical of neuropathies. To discribe the characteristics of IPs associated with these pathologies, a set of frequency domain discriptors, activity, mobility, and complexity were used, as well as several measures of the spectral density function. These discriptors demonstrate distinct patterns of variation corresponding to morphological changes observed in disease states, and closely with results obtained from the classical method, turn/amp technique.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.525-530
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• 2019

Electroencephalography(EEG) studies that have been in clinical research since the discovery of brainwave have recently been developed into brain-computer interface studies. Currently, research is underway to manipulate robot arms and drones by analyzing brainwave. However, resolution and reliability of EEG information is still limited. Therefore, it is required to develop various technologies necessary for measuring and interpreting brainwave more accurately. Pioneering new applications with these technologies is also important. In this paper, we propose development of a personal authentication system fit for lifecaretainment based on EEG. The proposed system guarantees the resolution and reliability of EEG information by using the Electrooculogram and Electromyogram(EMG) together with EEG.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.1
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• pp.119-128
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• 2017

G-induced Loss of Consciousness(G-LOC) can be predicted by measuring Electromyogram(EMG) signals. Existing studies have mainly focused on specific body parts and lacked of consideration with quantitative EMG indices. The purpose of this study is to analyze the indices of EMG signals by human body parts for monitoring G-LOC condition. The data of seven EMG features such as Root Mean Square(RMS), Integrated Absolute Value(IAV), and Mean Absolute Value(MAV) for reflecting muscle contraction and Slope Sign Changes(SSC), Waveform Length (WL), Zero Crossing(ZC), and Median Frequency(MF) for representing muscle contraction and fatigue was retrieved from high G-training on a human centrifuge simulator. A total of 19 trainees out of 47 trainees of the Korean Air Force fell into G-LOC condition during the training in attaching EMG sensor to three body parts(neck, abdomen, calf). IAV, MAV, WL, and ZC under condition after G-LOC were decreased by 17 %, 17 %, 18 %, and 4 % comparing to those under condition before G-LOC respectively. Also, RMS, IAV, MAV, and WL in neck part under condition after G-LOC were higher than those under condition before G-LOC; while, those in abdomen and calf part lower. This study suggest that measurement of IAV and WL by attaching EMG sensor to calf part may be optimal for predicting G-LOC.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.2
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• pp.126-134
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• 1990

In this study, eight function discrimination and recognition of the EMG signal from the biceps and triceps of 4 subjects were executed, using the Euclidean and weighted cepstral distance measure with LPC cepstrum coefficients. In case of Euclidean cepstral distance measure, as the number of LPC cepstrum coefficients was increased in 8, 10, 12, 14 the recognition rates of functions are 94.69, 95.63, 96.56, and 96.88[%], respectively, but increasing rates of recognition were inclined to decrease. In case of weighted cepstral distance measure, when the number of LPC cepstrum coefficients was 8, 10, 12 and 14, the recognition rates of functions were 91.88, 95, 99.69, and 96.63[%], respectively.

• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.227-237
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• 2014

The purpose of this paper is to implement a multiple bio-signal central monitoring system based on textile fabrics flexible platform which can obtain and monitor bio signals(heart rate, body temperature, electrocardiography, electromyogram) of workers in special working environments and additional situational information (3-axis acceleration, temperature, humidity, illumination, surrounding image). This system can prevent various accidents that may occur in the remote work environment and provide fast and efficient response by detecting workers' situations in real-time. For it, the textile fabrics flexible platform was made as innerwear or outerwear so that it does not interfere with workers' performance while collecting bio-signal and situational information, and obtained information is sent to the central monitoring system through wireless communication. The central monitoring system is based on wireless medical telemetry service of WMTS (Wireless Medical Telemetry Service); can monitor from 2 to 32 people simultaneously; and was designed so that it can be expanded. Also, in this study, to verify performance of the WMTS communication model, packet transmission rates were compared according to the distance.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.40-48
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• 2009

Leading of the computer, IT technology has make great strides. As a information-industry-community was highly developed, user's needs to convenience about intelligence and humanization of interface is being increase today. Nowadays, researches with are related to BCI are progress put the application-technology development first in importance eliminating research about fountainhead technology with DB construction. These problems are due to a BCI-related research studies have not overcome the initial level, and not toward a systematic study. Brain wave are collected from subjects is a signal that the signal is appropriate and necessary in the experiment is difficult to distinguish. In addition, brain wave that it's not necessary to collect the experiment, serious eyes flicker, facial and body movements of an EMG and electrodes attached to the state, noise, vibration, etc. It is hard to collect accurate brain wave was caused by mixing disturbance wave in experiment on the environment. This movement, and the experiment of subject impact on the environment due to the mixing disturbance wave can cause that lowering cognitive and decline of efficiency when embodied BCI system. Therefore, in this paper, we propose an accurate and efficient brain-wave DB building system that more exactness and cognitive basis studies when embodied BCI system with brain-wave. For the minimize about brain wave DB with mixing disturbance, we propose a DB building method using an automatic control and prevent unnecessary action, put to use the subjects face tracking.

• Sleep Medicine and Psychophysiology
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• v.25 no.2
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• pp.82-91
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• 2018

Objectives: Synchronous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been used to explore sleep stage dependent functional brain networks. Despite a growing number of sleep studies using EEG-fMRI, few studies have conducted network analysis on whole night sleep due to difficulty in data acquisition, artifacts, and sleep management within the MRI scanner. Methods: In order to perform network analysis for whole night sleep, we proposed experimental procedures and data processing techniques for EEG-fMRI. We acquired 6-7 hours of EEG-fMRI data per participant and conducted signal processing to reduce artifacts in both EEG and fMRI. We then generated a functional brain atlas with 68 brain regions using independent component analysis of sleep fMRI data. Using this functional atlas, we constructed sleep level dependent functional brain networks. Results: When we evaluated functional connectivity distribution, sleep showed significantly reduced functional connectivity for the whole brain compared to that during wakefulness. REM sleep showed statistically different connectivity patterns compared to non-REM sleep in sleep-related subcortical brain circuits. Conclusion: This study suggests the feasibility of exploring functional brain networks using sleep EEG-fMRI for whole night sleep via appropriate experimental procedures and signal processing techniques for fMRI and EEG.