• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.133-136
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• 2002

EEG(Electroencephalography) is generated by electrical activity between neurons in cortical. Waveform of EEG is changed according to body and mental states. Therefore EEG is used to diagnosis of encephalophyma and epilepsy, etc. Also EEG is used to HCI(Human-Computer Interface). This paper describes estimation of orientation and location of dipole sources. The forward model is three-layer spherical head model and current dipole model. Using analytical solution, EEG is generated. Using MNLS(Minimum-Norm Least-Square) method, orientation and location of dipole moment is estimated.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.11a
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• pp.250-254
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• 2002

컴퓨터의 발달과 더불어 인간과 컴퓨터 인터페이스에 있어서도 많은 발전을 하고 있다. 본 연구는 두뇌-컴퓨터 인터페이스(brain-computer interface : BCI)를 위해서 인간에 있어서 가장 간단한 의사문제라고 여겨지는 긍정이나 부정을 선택할 때 나타나는 뇌파를 AR 모델을 이용하여 시간-주파수 분석을 한 후 topographical map을 그렸다. 그 결과 문제에 대답하는 시점 전후에서 파워스펙트럼이 유사하였고, 피험자가 문제를 읽고 판단하고, 동작하는 시점(reaction time : RT) 전으로 1초 ~ 0.5초 사이에 전두엽과 두정엽 부위에서 16Hz ~ 24Hz, 80 ∼ 88Hz의 주파수 대역에서 유의미한 차이를 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.801-804
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• 2002

뇌기능 연구 수단으로 널리 사용되고 있는 뇌파의 시각적 분석 및 정량적 분석시 오차를 증가시키는 원인이 되어 왔던 잡파(artifact)를 제거 대상이 아닌 제어 신호로써 활용한다. 본 연구에서는 다양한 잡파 중 뇌파 측정시 가장 잘 포함되고, 시각적으로 쉽게 구별이 가능한 안면근(facial muscle) 신호를 이용한다. 측정된 뇌파에 파워스펙트럼(power spectrum)을 적응하여 뇌파를 분석하고, Backpropagation 알고리즘을 이용하여 전 처리된 뇌파를 인식하는 2 채널 실시간 인식(recognition) 및 분류(classification) 시스템을 구현한다. 이와 같이 구현된 시스템을 이용하여 5 방향(상, 하, 좌, 우, 정지) 제어를 실시함으로써 뇌-컴퓨터간 통신을 통한 방향제어 시스템을 구현하였다.

• ETRI Journal
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• v.43 no.6
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• pp.1081-1092
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• 2021

Electroencephalography (EEG) recordings taken during the perception of music tempo contain information that estimates the tempo of a music piece. If information about this tempo stimulus in EEG recordings can be extracted and classified, it can be effectively used to construct a music-based brain-computer interface. This study proposes a novel convolutional recurrent attention model (CRAM) to extract and classify features corresponding to tempo stimuli from EEG recordings of listeners who listened with concentration to the tempo of musics. The proposed CRAM is composed of six modules, namely, network inputs, two-dimensional convolutional bidirectional gated recurrent unit-based sample encoder, sample-level intuitive attention, segment encoder, segment-level intuitive attention, and softmax layer, to effectively model spatiotemporal features and improve the classification accuracy of tempo stimuli. To evaluate the proposed method's performance, we conducted experiments on two benchmark datasets. The proposed method achieves promising results, outperforming recent methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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• pp.81-87
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• 2023

With the development of BCI devices, it is now possible to use EEG control technology to move the robot's arms or legs to help with daily life. In this paper, we propose a customized vehicle control model based on BCI. This is a model that collects BCI-based driver EEG signals, determines information according to EEG signal analysis, and then controls the direction of the vehicle based on the determinated information through EEG signal analysis. In this case, in the process of analyzing noisy EEG signals, controlling direction is supplemented by using a camera-based eye tracking method to increase the accuracy of recognized direction . By synthesizing the EEG signal that recognized the direction to be controlled and the result of eye tracking, the vehicle was controlled in five directions: left turn, right turn, forward, backward, and stop. In experimental result, the accuracy of direction recognition of our proposed model is about 75% or higher.

• Journal of Biomedical Engineering Research
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• v.36 no.6
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• pp.283-290
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• 2015

We proposed blink based online speller for the locked-in syndrome (LIS) patients, paralyzed in nearly all voluntary muscles expect for the eyes, with a simple and easy-to-use eye blink detection glasses. Electrooculogram (EOG) is the golden standard method of eye movement or blink measurement with Ag/AgCl electrodes. However, this method has several drawbacks such as skin irritation and dehydration of conductive gel. To resolve the shortcomings, we used a blink detection system based on a transparent capacitively coupled electrode, which is conductive indium tin oxide (ITO) films. The films make it possible to measure eye blink without direct skin contact and obstruction of field of view. We finally developed user-friendly blink based online speller with the blink detection system. To classify voluntary and non-voluntary blink, we used the double blink for command of the speller. The online speller experiment result with six healthy subjects shows that mean accuracy is 98.96% and letter per minute (LPM) is 4.73, which are better result by comparison with conventional P300 or auditory brain-computer interface (BCI) paradigm. The result of the experiment demonstrates the possibility of applying the proposed system as a communication method for the LIS patients.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.1
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• pp.129-140
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• 2017

In this paper, a $4{\times}32$-channel neural recording system capable of acquiring neural signals is introduced. Four 32-channel neural recording ICs, complex programmable logic devices (CPLDs), a micro controller unit (MCU) with USB interface, and a PC are used. Each neural recording IC, implemented in $0.18{\mu}m$ CMOS technology, includes 32 channels of analog front-ends (AFEs), a 32-to-1 analog multiplexer, and an analog-to-digital converter (ADC). The mid-band gain of the AFE is adjustable in four steps, and have a tunable bandwidth. The AFE has a mid-band gain of 54.5 dB to 65.7 dB and a bandwidth of 35.3 Hz to 5.8 kHz. The high-pass cutoff frequency of the AFE varies from 18.6 Hz to 154.7 Hz. The input-referred noise (IRN) of the AFE is $10.2{\mu}V_{rms}$. A high-resolution, low-power ADC with a high conversion speed achieves a signal-to-noise and distortion ratio (SNDR) of 50.63 dB and a spurious-free dynamic range (SFDR) of 63.88 dB, at a sampling-rate of 2.5 MS/s. The effectiveness of our neural recording system is validated in in-vivo recording of the primary somatosensory cortex of a rat.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.7-13
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• 2018

In the current study, we proposed an optimized brain-computer interface (BCI) which employed blind source separation (BBS) approach to remove noises. Thus motor imagery (MI) signal and steady state visual evoked potential (SSVEP) signal were easily to be detected due to enhancement in signal-to-noise ratio (SNR). Moreover, a combination between MI and SSVEP which is typically can increase the number of commands being generated in the current BCI. To reduce the computational time as well as to bring the BCI closer to real-world applications, the current system utilizes a single-channel EEG signal. In addition, a convolutional neural network (CNN) was used as the multi-class classification model. We evaluated the performance in term of accuracy between a non-BBS+BCI and BBS+BCI. Results show that the accuracy of the BBS+BCI is achieved $16.15{\pm}5.12%$ higher than that in the non-BBS+BCI by using BBS than non-used on. Overall, the proposed BCI system demonstrate a feasibility to be applied for multi-dimensional control applications with a comparable accuracy.

• Journal of KIISE
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• v.42 no.12
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• pp.1535-1543
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• 2015

The brain-computer interface obtains a user's electroencephalogram as a replacement communication unit for the disabled such that the user is able to control machines by simply thinking instead of using hands or feet. In this paper, we propose a feature extraction method based on a non-selected filter by SBCSP to classify motor imagery EEG. First, we divide frequencies (4~40 Hz) into 4-Hz units and apply CSP to each Unit. Second, we obtain the FLD score vector by combining FLD results. Finally, the FLD score vector is projected onto the optimal plane for classification using PCA. We use BCI Competition III dataset IVa, and Extracted features are used as input for LS-SVM. The classification accuracy of the proposed method was evaluated using $10{\times}10$ fold cross-validation. For subjects 'aa', 'al', 'av', 'aw', and 'ay', results were $85.29{\pm}0.93%$, $95.43{\pm}0.57%$, $72.57{\pm}2.37%$, $91.82{\pm}1.38%$, and $93.50{\pm}0.69%$, respectively.

• 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.