• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.285-290
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• 2020

Neuro-feedback is a technology that can identify your brain state and you can intentionally change your brain state. People with attention deficit and hyperactivity disorder need this technology but existing neuro-feedback training has a problem, which is not interesting and maintains a static state for a long time. In this paper, we proposed and implemented a neuro-feedback game that combines neuro-feedback and gun-shooting games to enhance concentration training. The neuro-feedback game has been implemented with the design of EEG measurement system, game controller and gamesoft. We hope that this study will be useful for people suffering from attention deficit and hyperactivity disorder.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.15-20
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• 2018

This study aimed to find an effective intervention measure through establishing the correlation between self-regulation (control over life style) and height growth through neuro-feedback training. 40 elementary students in grades two to four with height growth programs (20 experimental group students, 20 control group students) were examined for the changes before and after undergoing neuro-feedback training. The experiment lasted for three months with one 30-minute training session two times a week. After analyzing the differences in self-regulation among the control group with no neuro-feedback training and the experimental group with neuro-feedback training, the differences in height growth were analyzed. First of all, there were positive changes in self-regulation of the experimental group compared with the control group. Secondly, the experimental group showed larger changes in height growth. In conclusion, neuro-feedback training had positive effects upon the self-regulation that adjusts the acquired factors of height growth, which led to positive effects.

• The Korean Journal of Health Service Management
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• v.6 no.2
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• pp.111-119
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• 2012

The purpose of the study were to investigate the effect of the neuro-feedback program which improves brain function and stress reduction effect. The research design is one group pretest-posttest survey. 121 nursing students of a college in the Busan region took part in the study. Training involving the neuro-feedback program was conducted twice a week, 30 minutes per session, for a total of 10 weeks. The collected data was encoded and analyzed using SPSS 12.0 Version. The brain function and stress levels of the subjects were analyzed through the before-and-after results of the training were analyzed using a paired t-test. The results of the study showed that the BQ and SQ were enhanced as a result of the neuro-feedback. In particular, there were significant increases in the SRQ, ATQ, ACQ, and EQ of the BQ. SQ is correlated with the prevalence rate and resistance to disease, meaning not only psychological anxiety, uneasiness and excitement, but also physical anxiety and response to disease. Based on the study, by using the neuro-feedback training as a program for stress reduction, it is expected that nursing students will receive less stress from internal and external factors, and their ability to cope with stress will be enhanced.

• Advances in robotics research
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• v.1 no.2
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• pp.155-169
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• 2014

Study results in the last decades show that amount and quality of physical exercises, then the active participation, and now the cognitive involvement of patient in rehabilitation training are known of crux to enhance recovery outcome of motor dysfunction patients after stroke. Rehabilitation robots mainly have been developing along this direction to satisfy requirements of recovery therapy, or focusing on one or more of the above three points. Therefore, neuro-machine interaction based active rehabilitation robot has been proposed for assisting paralyzed limb performing designed tasks, which utilizes motor related EEG, UCSDI (Ultrasound Current Source Density Imaging), EMG for rehabilitation robot control and feeds back the multi-sensory interaction information such as visual, auditory, force, haptic sensation to the patient simultaneously. This neuro-controlled and perceptual rehabilitation robot will bring great benefits to post-stroke patients. In order to develop such kind of robot, some key technologies such as noninvasive precise detection of neural signal and realistic sensation feedback need to be solved. There are still some grand challenges in solving the fundamental questions to develop and optimize such kind of neuro-machine interaction based active rehabilitation robot.

• The Journal of Korean Physical Therapy
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• v.26 no.6
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• pp.442-448
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• 2014

Purpose: The objective of this study is to evaluate the effect of neuro-feedback training and transcutaneous electrical nerve stimulation (TENS) on stress, quantitative sensory threshold and pain in patients suffering from tension type headache. Methods: 22 participants who passed the preliminary evaluation were enrolled in the study and 11 participants were randomly assigned to each group. The control group (n=11) was subject to the TENS treatment of which was composed of a 20-minute session for 5 times a week during 4 weeks, and the experimental group (n=11) was subject to both neuro feedback training and TENS treatment for 10 minutes a day and 5 days a week during 4 weeks. The Perceived Stress Scale (PSS) was used to measure a level of stress and the quantitative sensory testing (QST) was used for the measurement of cold pain threshold (CPT) and heat pain threshold (HPT); A degree of pain was evaluated through the headache impact test-6 (HIT-6). Results: In comparision of all dependent variables between the control and subject groups, there were significant differences in stress, quantitative sensory threshold and pain after the treatment (p<0.05), and the experimental group showed significant differences in stress, CPT, HPT and pain (p<0.05) and the control group showed only a significant difference in HPT (p<0.05). Conclusion: Findings of this study demonstrate that the concomitant administration of the TENS treatment and neuro feedback training is effective on alleviation of stress, quantitative sensory threshold and pain in patients with tension type headache.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.1
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• pp.101-111
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• 1993

Many processes such as machining, injection-moulding and metal-forming are usually operated by hydraulic servo-systems. The dynamic characteristics of these systems are complex and highly non-linear and are often subjected to the uncertain external disturbances associated with the processes. Consequently, the conventional approach to the controller design for these systems may not guarantee accurate tracking control performance. An effective neuro-fuzzy controller is proposed to realize an accurate hydraulic servo-system regardless of the uncertainties and the external disturbances. For this purpose, first, we develop a simplified fuzzy logic controller which have multidimensional and unsymmetric membership functions. Secondly, we develop a neural network which consists of the parameters of the fuzzy logic controller. It is show that the neural network has both learning capability and linguistic representation capability. The proposed controller was implemented on a hydraulic servo-system. Feedback error learning architecture is adopted which uses the feedback error directly without passing through the dynamics or inverse transfer function of the hydraulic servo-system to train the neuro-fuzzy controller. A series of simulations was performed for the position-tracking control of the system subjected to external disturbances. The results of simulations show that regardless of inherent non-linearities and disturbances, an accuracy tracking-control performance is obtained using the proposed neuro-fuzzy controller.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.4
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• pp.15-26
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• 1998

Full car model is needed for investigating as a entire dynamics of vehicle. In this study, 7DOF of full car model's dynamics is selected. This paper proposes the output feedback controller based on optimal control theory. Input data and output data from the optimal controller are used for neural network system identification of the suspension system. To do system identification, neural network which has robustness against nonlinearities and disturbances is adapted. This study uses back-propagation algorithm to train a multil-layer neural network. After obtaining a neural network model of a suspension system, a neuro-controller is designed. Neuro-controller controls suspension system with off-line learning method and multistep ahead prediction model based on the neural network model and a neuro-controller. The optimal controller and the neuro-controller are designed and then, both performances are compared through. For simulation, sinusoidal and rectangular virtual bumps are selected.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.514-517
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• 2017

인간의 뇌에서 발생되는 뇌파를 분석해 실생활에 활용한다면 집중력, 기억력, 학습능력 등이 증가하는 효과를 볼 수 있다. 본 논문에서는 뇌파시장의 전망과 MindWave를 이용하여 RC카를 제어하고 뉴로피드백(Neuro-feedback)훈련을 통해 집중력향상에 도움을 주는 게임 개발 내용을 다룬다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.852-857
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• 2008

A new adaptive neuro-control algorithm for a SISO strict-feedback nonlinear system is proposed. All the previous adaptive neural control algorithms for strict-feedback nonlinear systems are based on the backstepping scheme, which makes the control law and stability analysis very complicated. The main contribution of the proposed method is that it demonstrates that the state-feedback control of the strict-feedback system can be viewed as the output-feedback control problem of the system in the normal form. As a result, the proposed control algorithm is considerably simpler than the previous ones based on backstepping. Depending heavily on the universal approximation property of the neural network (NN), only one NN is employed to approximate the lumped uncertain system nonlinearity. The Lyapunov stability of the NN weights and filtered tracking error is guaranteed in the semi-global sense.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1439-1450
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• 2013

This paper investigates a robust neuro-fuzzy control (NFC) method which can accurately follow the speed reference of an interior permanent magnet synchronous motor (IPMSM) in the existence of nonlinearities and system uncertainties. A neuro-fuzzy control term is proposed to estimate these nonlinear and uncertain factors, therefore, this difficulty is completely solved. To make the global stability analysis simple and systematic, the time derivative of the quadratic Lyapunov function is selected as the cost function to be minimized. Moreover, the design procedure of the online self-tuning algorithm is comparatively simplified to reduce a computational burden of the NFC. Next, a rotor angular acceleration is obtained through the disturbance observer. The proposed observer-based NFC strategy can achieve better control performance (i.e., less steady-state error, less sensitivity) than the feedback linearization control method even when there exist some uncertainties in the electrical and mechanical parameters. Finally, the validity of the proposed neuro-fuzzy speed controller is confirmed through simulation and experimental studies on a prototype IPMSM drive system with a TMS320F28335 DSP.