• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.609-614
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• 2014

Cerebellar ataxia is a steadily progressive neurodegenerative disease associated with loss of motor control, leaving patients unable to walk, talk, or perform activities of daily living. Direct motor instruction in cerebella ataxia patients has limited effectiveness, presumably because an inappropriate closed-loop cerebellar response to the inevitable observed error confounds motor learning mechanisms. Recent studies have validated the age-old technique of employing motor imagery training (mental rehearsal of a movement) to boost motor performance in athletes, much as a champion downhill skier visualizes the course prior to embarking on a run. Could the use of EEG based BCI provide advanced biofeedback to improve motor imagery and provide a "backdoor" to improving motor performance in ataxia patients? In order to determine the feasibility of using EEG-based BCI control in this population, we compare the ability to modulate mu-band power (8-12 Hz) by performing a cued motor imagery task in an ataxia patient and healthy control.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.1-6
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• 2014

In this study, we propose optimized RBFNNs based on night vision face recognition simulator with the aid of $(2D)^2$ PCA algorithm. It is difficult to obtain the night image for performing face recognition due to low brightness in case of image acquired through CCD camera at night. For this reason, a night vision camera is used to get images at night. Ada-Boost algorithm is also used for the detection of face images on both face and non-face image area. And the minimization of distortion phenomenon of the images is carried out by using the histogram equalization. These high-dimensional images are reduced to low-dimensional images by using $(2D)^2$ PCA algorithm. Face recognition is performed through polynomial-based RBFNNs classifier, and the essential design parameters of the classifiers are optimized by means of Differential Evolution(DE). The performance evaluation of the optimized RBFNNs based on $(2D)^2$ PCA is carried out with the aid of night vision face recognition system and IC&CI Lab data.

• Korean Journal of Cognitive Science
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• v.25 no.4
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• pp.343-384
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• 2014

In order to unravel the problems of the mind, today's cognitive science has expanded its perspective from the narrow framework of the past computer model or neuronal network model to the wider frameworks of interaction with the brain in interaction with the body in interaction with their environments. The theories of 'the extended mind', 'embodied mind', or 'enactive mind' appeared through such processes are working on a way to move into the environments while the problem to unravel the complex process of interactions between the mind, the body and the environments are left alone. This problem can be traced back as far as to Gibson and Maturana & Varela who tried at first to unravel the problem of the mind in terms of interaction between the brain, the body and there environments in 1960~70s. It's because Gibson stressed the importance of the 'affordance' provided by the environment while Maturana & Varela emphasized the 'autonomy' of auto-poiesis of life. However, it will be proper to say that there are invariants in the affordances provided by the environment as well as the autonomy of life in the state of structural coupling of the environment's variants and life's openness toward the environment. In this case, the confrontational points between Gibson and Maturana & Varela will be resolved. In this article, I propose Benjamin's theory of mimesis as a mediator of both theories. Because Benjamin's concept of mimesis has the process of making a constellation of the embodiment of the affordance and the enaction of new affordance into the environment at the same time, Gibson's concept of the affordance and Maturana & Varela's concept of embodiment and enaction will be so smoothly interconnected to circulate through the medium of Benjamin's concept of mimesis.