• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.359-365
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• 2022

Scholars are paying increasingly close attention to brain research and the creation of biological neural networks, artificial neural networks, artificial intelligence, neurochips, brain-computer interfaces, prostheses, new research instruments and methods, methods of treatment, as well as the prevention of neurodegenerative diseases based on these data. The authors of the study propose their hypothesis on the understanding of the phenomenon of consciousness that answers questions concerning the criteria of consciousness, its localization, and principles of operation. In the study of the hard problem of consciousness, the philosophical and scientific categories of consciousness, and prominent hypotheses and theories of consciousness, the authors distinguish "the area of the conscious mind", which encompasses several states of consciousness united by the phenomenon of integrated consciousness. According to the authors, consciousness is a kind of executor of the phenomenological idea of the "chalice", so the search for it should be conducted deeper than the processes in the power of thought consciousness and transconsciousness, to which integrated consciousness can act as a lever. However, integrated consciousness may have the capacity to transcend into lower states of consciousness, which requires further study.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.3
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• pp.133-140
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• 2015

Neural stimulating implantable medical devices (IMDs) have been widely used to treat neurological diseases or interface with sensory feedback for amputees or patients suffering from severe paralysis. More recent IMDs, such as retinal implants or brain-computer interfaces, demand higher performance to enable sophisticated therapies, while consuming power at higher orders of magnitude to handle more functions on a larger scale at higher rates, which limits the ability to supply the IMDs with primary batteries. Inductive power transmission across the skin is a viable solution to power up an IMD, while it demands high power efficiencies at every power delivery stage for safe and effective stimulation without increasing the surrounding tissue's temperature. This paper reviews various wireless neural stimulating systems and their power management techniques to maximize IMD power efficiency. We also explore both wireless electrical and optical stimulation mechanisms and their power requirements in implantable neural interface applications.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.742-745
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• 2004

If a personal robot is popularized like a personal computer in the future, many kinds of robots will appear and the number of manufacturers will increase as a matter of course. In such circumstances, it can be inefficient, in case each manufacturer makes a whole platform individually. The solutions for this problem are to modularize a robot component (hardware and software) functionally and to standardize each module. Each module is developed and sold by each special maker and a consumer purchases desired modules and integrates them. The standardization of a module includes the unification of electrical and mechanical interface. In this paper, the standard interfaces of modules are proposed and CMR(Component Modularized Robot)-P2 made with the modules(brain, sensor, mobile, arm) is introduced. In order to simplify and to make the modules light, a frame is used for supporting a robot and communication/power lines. The name of a method and the way to use that are defined dependently on the standard interfaces in order to use a module in other modules. Each module consists of a distributed object and that can be implemented in the random language and platform. The sensor, mobile and arm modules are developed on Pentium or ARM CPU and embedded Linux OS using the C programming language. The brain module is developed on Pentium CPU and Windows OS using the C, C++ and RPL(Robot Programming Language). Also tasks like pass planning, localization, moving, object perception and face perception are developed. In our test, modules got into gear and CMR-P2 executed various scenarios like guidance, errand and guarding completely.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.137-142
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• 2024

Recently, deep learning technology has become those methods as de facto standards in the area of medical data representation. But, deep learning inherently requires a large amount of training data, which poses a challenge for its direct application in the medical field where acquiring large-scale data is not straightforward. Additionally, brain signal modalities also suffer from these problems owing to the high variability. Research has focused on designing deep neural network structures capable of effectively extracting spectro-spatio-temporal characteristics of brain signals, or employing self-supervised learning methods to pre-learn the neurophysiological features of brain signals. This paper analyzes methodologies used to handle small-scale data in emerging fields such as brain-computer interfaces and brain signal-based state prediction, presenting future directions for these technologies. At first, this paper examines deep neural network structures for representing brain signals, then analyzes self-supervised learning methodologies aimed at efficiently learning the characteristics of brain signals. Finally, the paper discusses key insights and future directions for deep learning-based brain signal analysis.

• International Journal of Contents
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• v.12 no.3
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• pp.12-16
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• 2016

Brain computer interfaces (BCI) usually have focused on classifying the explicitly-expressed intentions of humans. In contrast, implicit intentions should be considered to develop more intelligent systems. However, classifying implicit intention is more difficult than explicit intentions, and the difficulty severely increases for subject independent classification. In this paper, we address the subject independent classification of implicit intention based on electroencephalography (EEG) signals. Among many machine learning models, we use the support vector machine (SVM) with radial basis kernel functions to classify the EEG signals. The Fisher scores are evaluated after extracting the gamma, beta, alpha and theta band powers of the EEG signals from thirty electrodes. Since a more discriminant feature has a larger Fisher score value, the band powers of the EEG signals are presented to SVM based on the Fisher score. By training the SVM with 1-out of-9 validation, the best classification accuracy is approximately 65% with gamma and theta components.

• Journal of KIISE
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• v.41 no.12
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• pp.1050-1057
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• 2014

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be used for a number of purposes in a variety of industries, such as to replace body parts like hands and feet or to improve user convenience. In this paper, we propose a method to decompose and extract motor imagery EEG signal using Empirical Mode Decomposition (EMD) and Fast Fourier Transforms (FFT). The EEG signal classification consists of the following three steps. First, during signal decomposition, the EMD is used to generate Intrinsic Mode Functions (IMFs) from the EEG signal. Then during feature extraction, the power spectral density (PSD) is used to identify the frequency band of the IMFs generated. The FFT is used to extract the features for motor imagery from an IMF that includes mu rhythm. Finally, during classification, the Support Vector Machine (SVM) is used to classify the features of the motor imagery EEG signal. 10-fold cross-validation was then used to estimate the generalization capability of the given classifier., and the results show that the proposed method has an accuracy of 84.50% which is higher than that of other methods.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.499-505
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• 2012

Objective: Research on the transformation of interaction between mobile machines and users through analysis on current gesture interface technology development trend. Background: For smooth interaction between machines and users, interface technology has evolved from "command line" to "mouse", and now "touch" and "gesture recognition" have been researched and being used. In the future, the technology is destined to evolve into "multi-modal", the fusion of the visual and auditory senses and "3D multi-modal", where three dimensional virtual world and brain waves are being used. Method: Within the development of computer interface, which follows the evolution of mobile machines, actively researching gesture interface and related technologies' trend and development will be studied comprehensively. Through investigation based on gesture based information gathering techniques, they will be separated in four categories: sensor, touch, visual, and multi-modal gesture interfaces. Each category will be researched through technology trend and existing actual examples. Through this methods, the transformation of mobile machine and human interaction will be studied. Conclusion: Gesture based interface technology realizes intelligent communication skill on interaction relation ship between existing static machines and users. Thus, this technology is important element technology that will transform the interaction between a man and a machine more dynamic. Application: The result of this study may help to develop gesture interface design currently in use.

• The Korean Society of Law and Medicine
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• v.24 no.2
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• pp.147-196
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• 2023

Advances in brain science have made it possible to stimulate the brain to treat brain disorder or to connect directly between the neuron activity and an external devices. Non-invasive neurotechnologies already exist, but invasive neurotechnologies can provide more precise stimulation or measure brainwaves more precisely. Nowadays deep brain stimulation (DBS) is recognized as an accepted treatment for Parkinson's disease and essential tremor. In addition DBS has shown a certain positive effect in patients with Alzheimer's disease and depression. Brain-computer interfaces (BCI) are in the clinical stage but help patients in vegetative state can communicate or support rehabilitation for nerve-damaged people. The issue is that the people who need these invasive neurotechnologies are those whose capacity to consent is impaired or who are unable to communicate due to disease or nerve damage, while DBS and BCI operations are highly invasive and require informed consent of patients. Especially in areas where neurotechnology is still in clinical trials, the risks are greater and the benefits are uncertain, so more explanation should be provided to let patients make an informed decision. If the patient is under guardianship, the guardian is able to substitute for the patient's consent, if necessary with the authorization of court. If the patient is not under guardianship and the patient's capacity to consent is impaired or he is unable to express the consent, korean healthcare institution tend to rely on the patient's near relative guardian(de facto guardian) to give consent. But the concept of a de facto guardian is not provided by our civil law system. In the long run, it would be more appropriate to provide that a patient's spouse or next of kin may be authorized to give consent for the patient, if he or she is neither under guardianship nor appointed enduring power of attorney. If the patient was not properly informed of the risks involved in the neurosurgery, he or she may be entitled to compensation of intangible damages. If there is a causal relation between the malpractice and the side effects, the patient may also be able to recover damages for those side effects. In addition, both BCI and DBS involve the implantation of electrodes or microchips in the brain, which are controlled by an external devices. Since implantable medical devices are subject to product liability laws, the patient may be able to sue the manufacturer for damages if the defect caused the adverse effects. Recently, Korea's medical device regulation mandated liability insurance system for implantable medical devices to strengthen consumer protection.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.59-64
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• 2015

In this paper, we found the usefulness of the deep belief network (DBN) in the fields of brain-computer interface (BCI), especially in relation to imagined speech. In recent years, the growth of interest in the BCI field has led to the development of a number of useful applications, such as robot control, game interfaces, exoskeleton limbs, and so on. However, while imagined speech, which could be used for communication or military purpose devices, is one of the most exciting BCI applications, there are some problems in implementing the system. In the previous paper, we already handled some of the issues of imagined speech when using the International Phonetic Alphabet (IPA), although it required complementation for multi class classification problems. In view of this point, this paper could provide a suitable solution for vowel classification for imagined speech. We used the DBN algorithm, which is known as a deep learning algorithm for multi-class vowel classification, and selected four vowel pronunciations:, /a/, /i/, /o/, /u/ from IPA. For the experiment, we obtained the required 32 channel raw electroencephalogram (EEG) data from three male subjects, and electrodes were placed on the scalp of the frontal lobe and both temporal lobes which are related to thinking and verbal function. Eigenvalues of the covariance matrix of the EEG data were used as the feature vector of each vowel. In the analysis, we provided the classification results of the back propagation artificial neural network (BP-ANN) for making a comparison with DBN. As a result, the classification results from the BP-ANN were 52.04%, and the DBN was 87.96%. This means the DBN showed 35.92% better classification results in multi class imagined speech classification. In addition, the DBN spent much less time in whole computation time. In conclusion, the DBN algorithm is efficient in BCI system implementation.