• Journal of Sensor Science and Technology
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• v.25 no.4
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• pp.257-263
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• 2016

Among several technologies related to human brain, Brain Computer Interface (BCI) system is one of the most notable technologies recently. Conventional BCI for direct communication between human brain and machine are discomfort because normally electroencephalograghy(EEG) signal is measured by using multichannel EEG sensor. In this study, we propose 2-channel EEG sensor-based application control system which is more convenience and low complexity to wear to get EEG signal. EEG sensor module and system algorithm used in this study are developed and designed and one of the BCI methods, Motor Imagery (MI) is implemented in the system. Experiments are consisted of accuracy measurement of MI classification and driving control test. The results show that our simple wearable system has comparable performance with studies using multi-channel EEG sensor-based system, even better performance than other studies.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2029-2030
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• 2011

이 논문에서는 최근 활발히 연구되고 있는 BCI 실험에서 센서 위치의 변화에 따른 정확도 감소를 줄이는 방법을 알아본다. 이를 위해 특징추출 방법에서 많이 사용되는 두 가지 방법 (Power Spectrum Density, Phase Lock Value) 을 비교 및 평가 한다. motor imagery BCI 실험 결과 phase정보를 이용하는 Phase Lock Value가 달라지는 센서 위치에 덜 민감하다는 것을 확인할 수 있었다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7508-7512
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• 2015

We propose a brain-machine interface(BMI) based human-robot interaction(HRI) platform which operates machines by interfacing intentions by capturing brain waves. Platform consists of capture, processing/mapping, and action parts. A noninvasive brain wave sensor, PC, and robot-avatar/LED/motor are selected as capture, processing/mapping, and action part(s), respectively. Various investigations to ensure the relations between intentions and brainwave sensing have been explored. Case studies-an interactive game, on-off controls of LED(s), and motor control(s) are presented to show the design and implementation process of new BMI based HRI platform.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.136-144
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• 2015

The EEG system can be classified as a wired or wireless device. Each device used for the medical or entertainment purposes. The collected EEG signals from sensor are analyzed using feature extractions. A wireless EEG system provides good portability and convenience, however, it requires a mobile system that has heavy computing power. In this paper a single board system is proposed to handle EEG signal processing for BCI applications. Unfortunately, the computing power of a single board system is limited unlike general desktop systems. Thus, parallel approach using multiple single board systems is investigated. The parallel EEG signal processing system that we built demonstrates superlinear speedup for an EEG signal processing algorithm.

• Journal of Sensor Science and Technology
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• v.21 no.2
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• pp.156-166
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• 2012

Over the past 15 years productive BCI research programs have arisen. Current mainstream EEG electrode setups permit efficient recordings but most of electrodes has the disadventages of need for skin preparation and gel application to correctly record signals. The new gel-free probe was adapted for EEG recording and it can be fixed to the scalp with the micro needle without neuro-gel. It use standard EEG cap for wearing electrodes on scalp so it is compatible with standard EEG electrodes. A comparison between electrode characteristics is achieved by performing simultaneous recordings with the gel electrodes and gel-free probe placed in parallel scalp positions on the same anatomical regions. The quality of EEG recordings for all two types of experimental conditions is similar for gel-electrodes and gel-free probe. Subjects also reported not having special tactile sensations associated with wearing of gel-free probes. According to our results, it is expected that gel-free probe can be adapted to BCI, BMI(Brain Machine Interface), HMI(Human Machine Interface) because of its simple application and comfortable wearing process.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.247-249
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• 2020

In the era of the 4th industrial revolution, the demand for convergence between ICT technologies is increasing in various fields. Accordingly, a new term that combines data, network, and artificial intelligence technology, DNA (Data, Network, AI) is in use. and has recently become a hot topic. DNA has various potential technology to be able to develop intelligent application in the real world. Therefore, this paper introduces the reviewed papers on the service image placement mechanism based on the logical fog network, the mobility support scheme based on machine learning for Industrial wireless sensor network, the prediction of the following BCI performance by means of spectral EEG characteristics, the warning classification method based on artificial neural network using topics of source code and natural language processing model for data visualization interaction with chatbot, related on DNA technology.

• Journal of Sensor Science and Technology
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• v.12 no.6
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• pp.289-297
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• 2003

In this paper, we propose accurate classification method of an EEG signals during a mental tasks. In the experimental task, subjects achieved through the process of responding to visual stimulus, understanding the given problem, controlling hand motions, and select a key. To recognize the subjects' selection time, we analyzed with 4 types feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, $\theta$, $\gamma$ waves. From the analysed features, we construct specific rules for each subject meta rules including common factors in all subjects. In this system, the architecture of the neural network is a three layered feedforward networks with one hidden layer which implements the error back propagation learning algorithm. Applying the algorithms to 4 subjects show 87% classification success rates. In this paper, the proposed detection method can be a basic technology for brain-computer-interface by combining with discrimination methods.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1427-1428
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• 2015

MEMS(Micro Electro-mechanical System) are getting attention as promising industry in the 21st century. Car air bags, acceleration sensors, and medical, information appliances are being actively applied in MEMS. This paper suggest the electrical electrodes of brain signal applied MEMS model and the prototype design for EEG signal amplification circuit. Also, we suggest an independent BCI(Brain Computer Interface) system with brain electrical signal of electrode models and wireless communication platform.

• Journal of Information Processing Systems
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• v.14 no.4
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• pp.1049-1061
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• 2018

As mobile devices such as smartphones and tablet PCs become more popular, users are becoming accustomed to consuming a massive amount of multimedia content every day without time or space limitations. From the industry, the need for user satisfaction investigation has consequently emerged. Conventional methods to investigate user satisfaction usually employ user feedback surveys or interviews, which are considered manual, subjective, and inefficient. Therefore, the authors focus on a more objective method of investigating users' brainwaves to measure how much they enjoy their content. Particularly for multimedia content, it is natural that users will be immersed in the played content if they are satisfied with it. In this paper, the authors propose a method of using a portable and dry electroencephalogram (EEG) sensor device to overcome the limitations of the existing conventional methods and to further advance existing EEG-based studies. The proposed method uses a portable EEG sensor device that has a small, dry (i.e., not wet or adhesive), and simple sensor using a single channel, because the authors assume mobile device environments where users consider the features of portability and usability to be important. This paper presents how to measure attention, gauge and compute a score of user's content immersion level after addressing some technical details related to adopting the portable EEG sensor device. Lastly, via an experiment, the authors verified a meaningful correlation between the computed scores and the actual user satisfaction scores.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.45-52
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• 2017

EEG signals that are the same as those that have the same disabled people. So, the EEG signals are becoming the next generation. In this paper, we propose an internet of things system that controls the indoor environment using EEG signal. The proposed system consists EEG measurement device, EEG simulation software and indoor environment control device. We use data as EEG signal data on emotional imagination condition in a comfortable state and logical imagination condition in concentrated state. The noise of measured signal is removed by the ICA algorithm and beta waves are extracted from it. then, it goes through learning and test process using SVM. The subjects were trained to improve the EEG signal accuracy through the EEG simulation software and the average accuracy were 87.69%. The EEG signal from the EEG measurement device is transmitted to the EEG simulation software through the serial communication. then the control command is generated by classifying emotional imagination condition and logical imagination condition. The generated control command is transmitted to the indoor environment control device through the Zigbee communication. In case of the emotional imagination condition, the soft lighting and classical music are outputted. In the logical imagination condition, the learning white noise and bright lighting are outputted. The proposed system can be applied to software and device control based BCI.