• Journal of Semiconductor Engineering
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• v.1 no.3
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• pp.88-98
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• 2020

As biosensors are widely used in the medical field, flexible devices compatible with live animals have aroused great interest. Especially, significant research has been carried out to develop implantable or skin-attachable devices for real-time bio-signal sensing. From the device point of view, various biosensor types such as field-effect transistors (FETs) and multi-electrode arrays (MEAs) have been reported as diverse sensing strategies. In particular, the flexible FETs and MEAs allow semiconductor engineering to expand its application, which had been impossible with stiff devices and materials. This review summarizes the state-of-the-art research on flexible FET and MEA biosensors focusing on their materials, structures, sensing targets, and methods.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2875-2878
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• 2003

In this paper, we propose an EEG-based mental state prediction method during a mental tasks. In the experimental task, a subject goes through the process of responding to visual stimulus, understanding the given problem, controlling hand motions, and hitting a key. Considering the subject's varying brain activities, we model subjects' mental states with defining selection time. EEG signals from four subjects were recorded while they performed three mental tasks. Feature vectors defined by these representations were classified with a standard, feed-forward neural network trained via the error back-propagation algorithm. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or cognitive decision discrimination methods.

• Proceedings of the Optical Society of Korea Conference
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• 2008.02a
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• pp.455-456
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• 2008

근적외선 분광법(Near-infrared spectroscopy, NIRS)은 대뇌피질에서의 혈류변화(oxy-, deoxyhemoglobin의 농도변화)를 비침습적으로 측정할 수 있는 방법이다. 본 논문에서는 향후 뇌-컴퓨터 접속기술(Brain computer interface)에 적용하기위한 초기 연구단계로, 쥐의 수염을 자극시 활성화되는 대뇌수염피질 영역에서의 혈류변화 및 전기신호를 동시에 측정하고 두 신호의 패턴을 분석한다.

• Journal of IKEEE
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• v.24 no.1
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• pp.269-274
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• 2020

This paper is to study the exoskeleton robot for the walking of the elderly and the disabled. We developed and tested an Exoskeletal robot with two axes of freedom for joint motion. The EEG and EMG signals were used to move the joints of the Exoskeletal robot. By analyzing the EMG signal, the control signal was extracted and applied to the robot to facilitate the walking operation of the walking assistance robot. In addition, the brain-computer interface technology is applied to perform the operation of the robot using brain waves, spontaneous electrical activities recorded on the human scalp. These two signals were fused to study the walking recognition method of the supporting robot leg.

• 전자공학회논문지 IE
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• v.43 no.1
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• pp.16-24
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• 2006

This paper is aimed to develope of an integrated BCI(Brain Computer Interface System) that make possible for simultaneous multichannel data process and used extra cellular neural activity from the vestibular system instead of electroencephalogram signals for more precision control. The electrical properties pre-amplifier are 47.6 dB of gain, 0.005 % of distortion at 100 Hz, 12M$\Omega$ of input impedance. Window discriminator used two CPU with difference role to increase processing speed so that sampling frequency was 87 kHz. The designed window discriminator has more not only two times in signal resolution power but also ten times in error discrimination power than commericially available discriminator. The proposed method decreases 100 times in amount of integrated data then BCI system during 100 ms.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.29-34
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• 2015

Brain-computer interface (BCI) is being studied for convenient life in various application fields. The purpose of this study is to investigate a changing electroencephalography (EEG) for precise motion of a robot or an artificial arm. Three subjects who participated in this experiment performed three-task: Grip, Move, Relax. Acquired EEG data was extracted feature data using two feature extraction algorithm (power spectrum analysis and multi-common spatial pattern). Support vector machine (SVM) were applied the extracted feature data for classification. The classification accuracy was the highest at Grip class of two subjects. The results of this research are expected to be useful for patients required prosthetic limb using EEG.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.122-127
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• 2009

In this paper, we measured EEG signals on frontal and Broca's area when subjects imagine to speak A or B or C or D. These signals were analyzed by Event-Related Spectral Perturbation (ERSP), Inter-Trial Coherence (ITC) and Event Related Potential (ERP) methods. As a result, high coherences were showed at 1$\sim$13Hz during 0$\sim$300ms after the stimuli of each character and P300 was seen clearly and there are several differences between the ERP results. However, unlike the motivation of this study to classify the characters, it is impossible that we can classify each intention or each character cause these differences. Nevertheless, this paper suggest an application system using this results so BCI can provide various services.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.37-44
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• 2023

As a primary technology of Industry 4.0, human-robot collaboration (HRC) requires additional measures to ensure worker safety. Previous studies on avoiding collisions between collaborative robots and workers mainly detect collisions based on sensors and cameras attached to the robot. This method requires complex algorithms to continuously track robots, people, and objects and has the disadvantage of not being able to respond quickly to changes in the work environment. The present study was conducted to implement a web-based platform that manages collaborative robots by recognizing the emotions of workers - specifically their perception of danger - in the collaborative process. To this end, we developed a web-based application that collects and stores emotion-related brain waves via a wearable device; a deep-learning model that extracts and classifies the characteristics of neutral, positive, and negative emotions; and an Internet-of-things (IoT) interface program that controls motor operation according to classified emotions. We conducted a comparative analysis of our system's performance using a public open dataset and a dataset collected through actual measurement, achieving validation accuracies of 96.8% and 70.7%, respectively.

• Journal of Biomedical Engineering Research
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• v.45 no.2
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• pp.101-107
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• 2024

Steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) is one of the promising systems that can serve as an alternative input device due to its stable and fast performance. However, one of the major bottlenecks is that some individuals exhibit no or very low SSVEP responses to flickering stimulation, known as SSVEP illiteracy, resulting in low performance on SSVEP-BCIs. However, a lengthy duration is required to enhance multiple SSVEP responses using traditional single-frequency transcranial alternating current stimulation (tACS). This research proposes a novel approach using dual-frequency tACS (df-tACS) to potentially enhance SSVEP by targeting the two frequencies with the lowest signal-to-noise ratio (SNR) for each participant. Seven participants (five males, average age: 24.42) were exposed to flickering checkerboard stimuli at six frequencies to determine the weakest SNR frequencies. These frequencies were then simultaneously stimulated using df-tACS for 20 minutes, and the experiment was repeated to evaluate changes in SSVEP responses. The results showed that df-tACS effectively enhances the SNR at each targeted frequency, suggesting it can selectively improve target frequency responses. The study supports df-tACS as a more efficient solution for SSVEP illiteracy, proposing further exploration into multi-frequency tACS that could stimulate more than two frequencies, thereby expanding the potential of SSVEP-BCIs.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.277-282
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• 2015

Braincomputer interface (BCI) technology is making advances in the field of humancomputer interaction (HCI). To improve the BCI technology, we study the changes in the electroencephalogram (EEG) signals for six levels of grip strength: 10%, 20%, 40%, 50%, 70%, and 80% of the maximum voluntary contraction (MVC). The measured EEG data are categorized into three classes: Weak, Medium, and Strong. Features are then extracted using power spectrum analysis and multiclass-common spatial pattern (multiclass-CSP). Feature datasets are classified using a support vector machine (SVM). The accuracy rate is higher for the Strong class than the other classes.