• The Journal of Korean Physical Therapy
• /
• v.15 no.2
• /
• pp.75-84
• /
• 2003

The purpose of this study was to review of mechanism and application methodology about mental practice. The mental practice is symbolic rehearsal of physical activity in the absence of any gross muscular movements. Human have the ability to generate mental correlates of perceptual and motor events without any triggering external stimulus, a function known as imagery, Practice produces both internal and external sensory consequences which are thought to be essential for learning to occur, It is for this reason that mental practice, rehearsal of skill in imagination rather than by overt physical activity, has intrigued theorists, especially those interested in cognitive process. Several studies in sport psychology have shown that mental practice can be effective in optimizing the execution of movements in athletes and help novice learner in the incremental acquisition of new skilled behaviors. There are many theories of mental practice for explaining the positive effect In skill learning and performance. Most tenable theories are symbolic learning theory, psyconeuromuscular theory, Paivio's theory, regional cerebral blood flow theory, motivation theory, modeling theory, mental and muscle movement nodes theory, insight theory, selective attention theory, and attention-arousal set theory etc.. The factors for influencing to effects of mental practice are application form, application period, time for length of the mental practice, number of repetition, existence of physical practice.

• Physical Therapy Korea
• /
• v.28 no.4
• /
• pp.235-244
• /
• 2021

Background: The body schema, which is constantly updated using somatosensory information, enables accurate movement. Since pain is reported as a possible source to alter the body schema, the left right judgement test (LRJT) has been widely used in the pain rehabilitation. However, there was a lack of consistency in the effect of the pain on the LRJT results, and for the effect of the LRJT as a part of intervention programs for pain patients. The deeper understand of the LRJT is necessary for better reproducibility, and to expand the therapeutic applications of the LRJT in the pain and musculoskeletal rehabilitation. Objects: This literature review aimed to understand the LRJT and to study the potential of the LRJT for therapeutic applications. Methods: The PubMed database was searched for studies relevant to LRJT. To establish the query set, the term was regarded from various perspectives. Results: The selected studies were classified into three categories: LRJT development, factors influencing LRJT, and therapeutic applications. Conclusion: Left right judgement test is the evaluation tool for the integrity of body schema as well as a tool for implicit motor imagery. Pain, proprioception, and other factors influence the performance of the LRJT.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.3
• /
• pp.199-203
• /
• 2016

A brain-computer interface or BCI provides an alternative method for acting on the world. Brain signals can be recorded from the electrical activity along the scalp using an electrode cap. By analyzing the EEG, it is possible to determine whether a person is thinking about his/her hand or foot movement and this information can be transferred to a machine and then translated into commands. However, we do not know which information relates to motor imagery and which channel is good for extracting features. A general approach is to use all electronic channels to analyze the EEG signals, but this causes many problems, such as overfitting and problems removing noisy and artificial signals. To overcome these problems, in this paper we used a new optimization method called the Fruit Fly optimization algorithm (FOA) to select the best channels and then combine them with CSP method to extract features to improve the classification accuracy by linear discriminant analysis. We also used particle swarm optimization (PSO) and a genetic algorithm (GA) to select the optimal EEG channel and compared the performance with that of the FOA algorithm. The results show that for some subjects, the FOA algorithm is a better method for selecting the optimal EEG channel in a short time.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.6
• /
• pp.774-779
• /
• 2012

Brain-computer interface based on motor imagery is a system that transforms a subject's intention into a control signal by classifying EEG signals obtained from the imagination of movement of a subject's limbs. For the new paradigm, we do not know which positions are activated or not. A simple approach is to use as many channels as possible. The problem is that using many channels causes other problems. When applying a common spatial pattern (CSP), which is an EEG extraction method, many channels cause an overfit problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a binary particle swarm optimization with channel impact factor in order to select channels close to the most important channels as channel selection method. This paper examines whether or not channel impact factor can improve accuracy by Support Vector Machine(SVM).