• ETRI Journal
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• v.44 no.4
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• pp.672-685
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• 2022

This article proposes a subject-independent application of brain-computer interfacing (BCI). A 32-channel Electroencephalography (EEG) device is used to measure imagined speech (SI) of four words (sos, stop, medicine, washroom) and one phrase (come-here) across 13 subjects. A deep long short-term memory (LSTM) network has been adopted to recognize the above signals in seven EEG frequency bands individually in nine major regions of the brain. The results show a maximum accuracy of 73.56% and a network prediction time (NPT) of 0.14 s which are superior to other state-of-the-art techniques in the literature. Our analysis reveals that the alpha band can recognize SI better than other EEG frequencies. To reinforce our findings, the above work has been compared by models based on the gated recurrent unit (GRU), convolutional neural network (CNN), and six conventional classifiers. The results show that the LSTM model has 46.86% more average accuracy in the alpha band and 74.54% less average NPT than CNN. The maximum accuracy of GRU was 8.34% less than the LSTM network. Deep networks performed better than traditional classifiers.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.847-850
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• 2016

뇌-컴퓨터 인터페이스(Brain-Computer Interface: BCI) 기술의 중요성 및 활용도가 증대됨으로써 EEG(electroencephalogram: EEG)기반의 사용자 인터페이스에 대한 개발 및 연구가 활발히 진행되고 있다. 그러나 뇌파 발생 훈련이 되어 있지 않은 사용자는 EEG 기반의 사용자 인터페이스를 사용하기가 어렵다. 따라서 본 논문에서는 향후 뇌파 훈련을 위한 시뮬레이터를 개발하고자, 그 전단계로 사용자에게서 공통적으로 정확도가 높게 측정되는 채널 및 특징점을 비교, 분석 하였다. 피험자 3명의 왼손 동작 상상과 오른손 동작 상상으로 발생된 EEG 생체신호로부터 ERD/ERS를 확인하고, 8개의 특징점을 추출하여 SVM 분류 알고리즘을 기반으로 정확도를 측정하였으며, ${\mu}$대역 채널 AF4, F4에서의 특징 MAV에서 가장 우수한 성능을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1396-1398
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• 2015

BCI (Brain Computer Interface)는 사람의 두뇌와 컴퓨터를 연결하는 '뇌-컴퓨터 인터페이스'를 나타내는 것이며 EEG(Electroencephalogram)을 주로 분석하여 인간의 행동이나 의도를 파악한다. 본 논문에서는 EEG를 이용한 행동인식의 하나로 졸음을 판단하는 방법을 제안한다. 제안방법에서는 MindWave를 이용하여 얻은 실험 데이터를 FFT를 이용하여 1초 단위로 스펙트럼을 분석하여 High-Alpha 영역의 시간에 따른 데이터 변화 패턴을 분석하여 졸음을 판단한다. 실험 결과, 100%의 최고 성능을 얻을 수 있었다.

• 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.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1333-1342
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• 2018

Until now, Electroencephalography(: EEG) has been the most important and convenient method for the diagnosis and treatment of epilepsy. However, it is difficult to identify the wave characteristics of an epileptic EEG signals because it is very weak, non-stationary and has strong background noise. In this paper, we analyse the effect of dimensionality reduction methods on Epileptic EEG feature selection and classification. Three dimensionality reduction methods: Pincipal Component Analysis(: PCA), Kernel Principal Component Analysis(: KPCA) and Linear Discriminant Analysis(: LDA） were investigated. The performance of each method was evaluated by using Support Vector Machine SVM, Logistic Regression(: LR), K-Nearestneighbor(: K-NN), Decision Tree(: DR) and Random Forest(: RF). From the experimental result, PCA recorded 75% of highest accuracy in SVM, LR and K-NN. KPCA recorded 85% of best performance in SVM and K-KNN while LDA achieved 100% accuracy in K-NN. Thus, LDA dimensionality reduction is found to provide the best classification result for epileptic EEG signal.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.178-183
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• 2013

In this study, we proposed a method for electroencephalogram (EEG) classification using invariant CSP at special channels for improving the accuracy of classification. Based on the naive EEG signals from left and right hand movement experiment, the noises of contaminated data set should be eliminate and the proposed method can deal with the de-noising of data set. The considering data set are collected from the special channels for right and left hand movements around the motor cortex area. The proposed method is based on the fit of the adjusted parameter to decline the affect of invariant parts in raw signals and can increase the classification accuracy. We have run the simulation for hundreds time for each parameter and get averaged value to get the last result for comparison. The experimental results show the accuracy is improved more than the original method, the highest result reach to 89.74%.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.139-147
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• 2015

Steady state visual evoked potential (SSVEP) has been actively studied because of its short training time, relatively higher signal-to-noise ratio, and higher information transfer rate. There are two popular analysis methods for SSVEP signals: power spectral density analysis (PSDA) and canonical correlation analysis (CCA). However, the PSDA is known to be vulnerable to noise due to the use of a single channel. Although conventional CCA is more accurate than PSDA, it may not be appropriate for the real-time SSVEP-based BCI system when it has short time window length because it uses sinusoidal signals as references. Therefore, the two methods are not efficient for the real-time BCI system that requires a short TW and a high recognition accuracy. To overcome this limitation of the conventional methods, this paper proposes a frequency recognition method with a combination of CCA and PSDA using the difference between powers of canonical variables obtained from the results of CCA. Experimental results show that the performance of the combination of CCA and PSDA is better than that of CCA for the case of a short TW.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.497-501
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• 2008

In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classily multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two data sets in BCI competition, confirm the useful behavior of the method for continuous EEG classification.