Earticle

A number of Brain-Computer Interface (BCI) studies have been performed to assess the cognitive status through EEG signal. However, there are a few studies trying to prevent user from unexpected safety-accident in BCI study. The EEGs were collected from 19 subjects who participated in two experiments (rest & event-related potential measurement). There was significant difference in EEG changes of both spontaneous and event-related potential. Beta power and P300 latency may be useful as a biomarker for prevention of response to safety-accident.

A number of Brain-Computer Interface (BCI) studies have been performed to assess the cognitive status through EEG signal. However, there are a few studies trying to prevent user from unexpected safety-accident in BCI study. The EEGs were collected from 19 subjects who participated in two experiments (rest & event-related potential measurement). There was significant difference in EEG changes of both spontaneous and event-related potential. Beta power and P300 latency may be useful as a biomarker for prevention of response to safety- accident.

뇌파에 관한 많은 기술적 발전에도 불구하고 정확한 뇌파의 측정은 오늘날에도 쉽지 않다. 더욱이 비침습 형 뇌파인식기를 이용한 뇌파의 측정은 정확도가 떨어진다. 하지만 실용적인 뇌-컴퓨터 인터페이스 활용 시스템 개발을 위해서는 사용의 편의성 때문에 비침습형 뇌파인식기를 이용하는 경우가 많다. 특히, 가장 사용하기 편한 비침습형 건식 뇌파인식기인 경우 뇌파 측정의 정확도가 가장 떨어진다. 본 논문에서는 임상 응용보다는 뇌-컴퓨 터 인터페이스 시스템 개발 시 뇌파 측정을 통한 사용자 의도 파악의 정확도를 높이는 데 필요한 뇌파에 대한 기 초 지식과 특성에 대해 알아본다. 뇌파 측정을 통한 사용자 의도 파악이 정확해지면 다양한 분야에서 뇌파의 활용 도가 높아지고 새로운 응용 분야도 개척될 것이다.

Despite many technological advances in EEG, accurate EEG measurement is not easy even today. Moreover, the accuracy of EEG measurement using a non-invasive EEG recognizer is low. However, for the development of a practical brain-computer interface utilization system, a non-invasive brain wave recognizer is often used because of its ease of use. In particular, the accuracy of EEG measurement of non-invasive dry brain wave recognizer which is the most convenient one is the lowest. In this paper, we examine the basic knowledge and characteristics of EEG needed to improve the accuracy of user intention identification through EEG measurement when developing a brain-computer interface system rather than clinical applications. If the user's intention through EEG measurement becomes accurate, the utilization of EEG in various fields will increase and new application fields will be pioneered.

최근 뇌-컴퓨터 인터페이스 관련 연구가 활발히 이루어지고 있다. 뇌파 시스템은 간단하고 저렴한 뇌파 측 정 장치로 사용자의 의도를 정확히 측정할 수 있어야 실용적인 시스템이 개발될 수 있다. 현재 뇌파 측정의 기술적 한계로 인해 휴대용 뇌파측정기기에 의한 신호 측정 및 해석의 신뢰성을 보장하기가 어렵다. 딥러닝 기술을 사용하 는 것은 뇌파 해석의 신뢰성을 향상시키기 위한 한 방법이 될 수 있다. 딥러닝은 다양한 모델을 이용하여 수행될 수 있으며 모델의 선정은 문제해결의 효율성과 직결된다. 본 논문에서는 건식 전극과 배터리를 사용한 휴대용 뇌파 측정기기를 이용하여 뇌-컴퓨터 인터페이스 개발 시 뇌파 해석의 정확도 향상을 위해 필요한 딥러닝의 기초 기술에 대하여 알아본다. 후속 연구에서는 서로 다른 모델을 이용한 뇌파 데이터 딥러닝의 결과를 비교 분석 한다.

Recently, brain-computer interface related research has been actively conducted. The EEG system should use a simple and inexpensive EEG measurement device, in order to be a practical system that can measure the user's intention correctly. Due to the limitations of current EEG signal detection, it is difficult to guarantee the reliability of signal measurement and interpretation using portable EEG measuring devices. Applying deep learning technique could be one way to improve the reliability of EEG measurements. Deep learning can be performed using various models, and the selection of a model is directly related to the efficiency of problem solving. In this paper, we will look into the basic deep learning technology required for the effective EEG interpretation when developing a brain-computer interface using an EEG measuring device with a dry electrode and a battery. The deep learning results of EEG with different models will be compared and analysed in the following research.

This article aims at analyzing the 2014 football world cup kick-off, which highlighted a paraplegic person equipped with an “exoskeleton guided by thought”. In a first part, the ritualistic framework which surrounds the event is described. It seems to establish a mediation of a symbolic or even religious nature, conducive to building trust in an announced “miracle”. In a second part, the event itself is described in a factual fashion. It is clearly proven that the miracle didn't occur. Some elements of media coverage are provided, as well as internet users' reactions. It seems there was a distortion in the perceptions of the event. In a third part, after having shown that a rational reading of the announcement seemed to imply that it was impossible to carry out, a theoretical framework with a psychosocial inspiration is suggested to attempt to better explain the distortions in individuals' perceptions, during this event.

Objective: To explore the perspectives of individuals with high cervical spinal cord injury (C-SCI) regarding expectations, concerns, and desired applications of brain?computer interface (BCI).Methods: A structured focus group interview was conducted with four individuals with chronic high cervical spinal cord injury, all with neurological levels of C4 or above. Pre- and post-interview questionnaires were administered to assess expectations, concerns, and acceptance of BCI before and after discussion. The interview was facilitated by experts in rehabilitation medicine and biomedical engineering, and qualitative data were analyzed inductively to identify key themes.Results: Five major themes emerged: digital accessibility, offline physical activity, social interactions and psychological health, usability, and acceptance. Participants expressed strong interest in using BCI to improve digital independence, particularly for messaging, online banking, internet use, and smart home control. They also viewed BCI as a potential tool to enhance autonomy in daily activities and reduce reliance on caregivers. Most participants were open to training and, in some cases, invasive procedures; however, concerns regarding surgical safety, device maintenance, reliability, and practical usability were frequently raised. Views on the emotional and social impact of BCI varied across individuals. Questionnaire responses showed increased willingness to undergo invasive procedures after the focus group interview, while expected functional outcomes became more realistic, suggesting that structured group discussion may have shaped participants’ understanding of BCI.Conclusion: BCI was perceived as a promising pathway to greater independence, participation, and autonomy among individuals with high C-SCI. These findings emphasize user-centered design and demonstrate how lived experience can guide assistive neurotechnology development in rehabilitation research.

본 연구는 비교사학습의 대표적인 방법 중 하나인 코호넨의 자기조직화 방법을 기반으로 BCI(Brain-Computer Interface)에 적용 가능한 자율적 기계학습방법을 제안한다. 이를 위해 상호작용 함수를 이용한 학습영역조정방법과 자율적 기계학습규칙을 제안하였다. 학습영역조정과 기계학습은 코호넨의 자기조직화 방법을 기반으로 한 상호작용 함수에 의한 측면제어효과를 이용하였다. 승자 뉴런을 결정하고 난 후 학습 규칙에 따라 뉴런의 연결강도를 조정하 고 학습 횟수가 증가함에 따라 학습영역이 점차 감소하여 출력층 뉴런 가중치들의 입력을 향한 유동을 완화시켜 네 트워크가 평형 상태(equilibrium state)에 도달하여 학습을 마칠 수 있는 자율적 기계학습을 제안하였다.

This paper proposes an autonomous machine learning method applicable to the BCI(Brain-Computer Interface) is based on the self-organizing Kohonen method, one of the exemplary method of unsupervised learning. In addition we propose control method of learning region and self machine learning rule using an interactive function. The learning region control and machine learning was used to control the side effects caused by interaction function that is based on the self-organizing Kohonen method. After determining the winner neuron, we decided to adjust the connection weights based on the learning rules, and learning region is gradually decreased as the number of learning is increased by the learning. So we proposed the autonomous machine learning to reach to the network equilibrium state by reducing the flow toward the input to weights of output layer neurons.

뇌-컴퓨터 인터페이스(BCI: Brain Computer Interface)는 뇌파를 활용하여 뇌의 활동이 컴퓨터에 직접 입력되 어, 마우스나 키보드 같은 입력장치가 없이도 컴퓨터와 커뮤니케이션을 할 수 있는 장치를 말한다. 뇌파 인터페이스 관련 하드웨어제작기술이 발전함에 따라 고가이면서 대형이었던 뇌파측정장비가 최근에는 소형화되고 저렴한 가격대 로 출시되면서 앞으로 다양한 멀티미디어 분야에서 응용이 될 것으로 예상된다. 본 논문은 BCI의 다양한 연구 가운 데 일반인들이 가장 먼저 접할 수 있는 응용영역인 게임에 대하여 BCI가 어떻게 적용되고 있는지 현재까지의 국내 외 기술수준과 동향을 파악하고자 한다. 다음으로 BCI를 사용한 게임의 문제점을 살펴보고 향후 국내 BCI 연구 및 개발방향을 제시하고자 한다.

Brain-computer interface is (BCI) is a communication device that the brain activity is directly input to the computer without input devices, such as a mouse or keyboard. As the brain wave interface hardware technology evolves, expensive and large EEG equipment has been downsized cheaply. So it will be applied to various multimedia applications. Among BCI studies, we suggest the domestic and foreign research trend about how the BCI is applied about the game almost people use. Next, look at the problems of the game with the BCI, we would like to propose the future direction of domestic BMI research and development.

This paper describes design and implementation of a three-dimensional game based on a brain-computer interface (BCI). The interaction between player and game application is controlled by translating brain signals into user-key input through electroencephalogram software. To evaluate the game performance, we compare the scores of the BCI interface with those of an existing non-BCI interface. The experiments results show that the players can control the game with BCI although the game speed is low.

The goal of the present study is to investigate oscillatory features of electroencephalogram in individuals of different ABO blood types with the ultimate aim of identifying distinctive features between blood types in physiological signals. EEG signals have been recorded by four electrodes on scalp from 25 subjects at resting state with eyes open. The power spectral densities have been estimated and analyzed in each of the four frequency bands from 4 to 50 Hz. Statistical analysis and classification using the support vector machines have been carried out, and significant differences are found among subjects with different ABO blood types. Our results indicate that the frequency analysis of EEG data is significantly contingent upon ABO blood type.

Directly from the brain thinking activity signals to communicate with the outside world, to achieve the heart and heart communication, achieve control of the surrounding environment, even is the dream of human beings since the ancient times is the pursuit of. Brain-computer Interface (Brian - Computer Interface: BCI) this novel human-computer interaction mode provides the scientific way to realize this dream. People hope that the new communication technology can be used in traffic tools, weapons, and other auxiliary control system, especially for those neuromuscular damage, cannot use the conventional methods of communication disability patients provides another way to communicate with the outside world. Exercise imagination refers to through the brain consciously simulate a certain action, but without obvious physical activity. In the human brain has a corresponding motor cortex area, when people have limbs activities, the motor cortex area is active. In imagine movement, although physical activity, but has remained active in the areas of the brain's corresponding motor cortex, the brain also sends out the corresponding EEG signals, so that there will be movement similar brain electrical signal, but due to the body don't exercise, avoid the my electricity interference, using the movement of the thought mainly, participants imagine left and right hand movement, or don't want to, the need to constantly training, participants learn to imagine the essence of sport, to avoid other distractions. So-called brain-computer interface, it is an organization that does not depend on peripheral nerves and muscles, etc. Usually the brain output channel of communication system. In recent five years, the research of this field gradually formed a hotspot; dozens of research team in the world have developed various forms of BCI experiment system. This research mainly based on multiple electrodes EEG recording, for a variety of brain stimulation mode is intended to explore the spatial and temporal variations of electrical signals. Applied to the second-order blind identification, phase synchronization and energy entropy of the signal analysis methods to analyze imagine movement EEG signals processing, extracting its features, and USES the BP neural network and support vector machine (SVM) classification method for different types of EEG classification is imagine movement, won a higher classification accuracy and designed a BCI system based on motion imagination, through this system, participants can more freely to imagine to control the mouse movement or virtual car movement to the left or right. The innovation of this study is to imagine the movement of brain electrical signal as input signal of the brain-computer interface system, imagination is a very complicated process, and the brain electrical signal characteristic is not obvious, so higher requirements for feature extraction and classification algorithm.

Epilepsy is a neurological disorder prevalent worldwide affecting individuals not just physically but also mentally and socially causing irreversible damages. It is caused when brain neurons are unable to regulate electrical signals resulting in seizures. The diagnosis of this life threatening disorder is thus critical. The current EEG test is one of the most common and effective epilepsy diagnosis medium, however it is prone to human error and time consuming. Therefore, we propose an Artificial Intelligence model that can diagnose these seizures with accuracy. Our model implements a combination of Convolutional Neural Network and Long Short-Term Memory architecture. Trained on 80% of total EEG recordings of 129 files with one or more seizures and 198 seizure events, this model used data of 23 pediatric subjects between the age of 3 -22 with 5 males, 17 females and 1 undisclosed. Data recordings with 10-20 systems of EEG electrode positions were obtained from Children’s Hospital Boston. The EEG signals obtained from the data were first segmented into fixed length windows appropriate for model input. Then they were normalized for a consistent signal amplitude. To remove background noises and artifacts, these processed recordings were filtered and then the recording segments were labeled based on the presence or absence of epileptic seizures. The data was then split into 80% training and 20% testing sets. For spatial feature extraction and capturing temporal dependencies in EEG signals CNN and LSTM models were implemented. The model was then cross validated. A confusion matrix was generated to visualize true and predicted classifications and an accuracy of 90% was achieved for currently available datasets. We are planning to include a wide range of EEG recordings with diverse age ranges and conditions to improve reliability. We are trying enhance accuracy by adding extra preprocessing steps. To conclude, this paper presents a methodological approach to analyzing brain activity via EEG dataset with the goal of epileptic detection without claiming medical accuracy or effectiveness.

Brain-computer interface (BCI) is combination of hardware and software systems that allows the severely or partially disabled persons to communicate with their surroundings. The study of brain activities precisely is an important step in BCI system. Many invasive and non-invasive neuro-imaging techniques are being conducted. In this paper, a comparative analysis of these different approaches has been reviewed such as electro-encephalography (EEG), electro-corticograph (ECoG), magneto-encephalograph (MEG), intra-cortical neuron recording (INR), and magnetic resonance imaging (MRI).

In this paper, we present a simple classification model based on statistical features and demonstrate the successful implementation of a brain-computer interface (BCI) based light on/off control system. This research shows study and development of light on/off control system based on BCI technology, which allows the users to control switching a lamp using electroencephalogram (EEG) signals. The logistic regression algorithm is used for classification of the EEG signal to convert it into light on, light off control commands. Training data were collected using 14-channel BCI system which records the brain signals of participants watching a screen with flickering lights and saves the data into .csv file for future analysis. After extracting a number of features from the data and performing classification using logistic regression, we created commands to switch on a physical lamp and tested it in a real environment. Logistic regression allowed us to quite accurately classify the EEG signals based on the user's mental state and we were able to classify the EEG signals with 82.5% accuracy, producing reliable commands for turning on and off the light.

Ordinal Pattern analysis has been used recently for extracting qualitative information from non-linear time series and it has been applied to usefully track brain dynamics. In this paper, we proposed a novel P300-based BCI system which depends on ordinal time series analysis as a feature extraction method. We have shown that this method can efficiently revel P300 feature, and therefore good classification accuracies and bitrates have been achieved for healthy and disabled subjects.

Recently, methodologies for developing brain-computer interface (BCI) games using the BCI have been actively researched. The existing general framework for processing brain waves does not provide the functions required to develop BCI games. Thus, developing BCI games is difficult and requires a large amount of time. Effective BCI game development requires a BCI game framework. Therefore the BCI game framework should provide the functions to generate discrete values, events, and converted waves considering the difference between the brain waves of users and the BCIs of those. In this paper, BCI game frameworks for processing brain waves for BCI games are proposed. A variety of processes for converting brain waves to apply the measured brain waves to the games are also proposed. In an experiment the frameworks proposed were applied to a BCI game for visual perception training. Furthermore, it was verified that the time required for BCI game development was reduced when the framework proposed in the experiment was applied.

Recent advances in computer science enabled people with severe motor disabilities to use brain-computer interfaces (BCI) for communication, control, and even to restore their motor disabilities. This paper reviews the most recent works of BCI in stroke rehabilitation with a focus on methodology that reported on data collected from stroke patients and clinical studies that reported on the motor improvements of stroke patients. Both types of studies are important as the former advances the technology of BCI for stroke, and the latter demonstrates the clinical efficacy of BCI in stroke. Finally some challenges are discussed.

인간 활동의 전 영역을 총괄하는 대뇌정보기능을 대표하는 뇌파는 대뇌피질에서 발현된다고 알려져 있다. 의학적인 연구 결과에 의하면 인지 사고 등의 역동적인 지식 활동, 다양한 감성 행동, 및 고차원적인 정신활동까지도 뇌파 분석을 통해서 어느 정도는 기계적인 인식이 가능한 것으로 알려져 있다. 뇌-컴퓨터 인터페이스는 인간 중심의 시스템을 위한 핵심 연구로서 뇌파 신호 분석에 의한 사용자 의도 인식 시스의 개발을 목표로 한다. 이에 따라서, 범용적으로 적용 가능한 뇌파신호 분석 기법 및 자동 처리 시스템에 관한 연구가 활발히 진행 중이다. 특히, 뇌는 부위별로 그 기능이 세분화 되어 있으며 의식 상태와 정신활동에 따라 뇌파가 수시로 변하면서 특정한 패턴을 갖는다. 이러한 뇌의 정보처리 메커니즘을 밝혀내면 전자장치와의 통신 인터페이스를 통해 기기를 제어할 수 있다. 본 논문은 사용자의 의도를 분석하는 방법과 이를 통해 다른 장치의 인터페이스를 제어할 수 있는 시스템을 설계했다.