• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.197-206
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• 2014

Motor imagery based Brain-computer Interface(BCI), which has recently attracted attention, is the technique for decoding the user's voluntary motor intention using Electroencephalography(EEG). For classifying the motor imagery, event-related desynchronization(ERD), which is the phenomenon of EEG voltage drop at sensorimotor area in ${\mu}$-band(8-13Hz), has been generally used but this method are not free from the performance degradation of the BCI system because EEG has low spatial resolution and shows different ERD-appearing band according to users. Common spatial pattern(CSP) was proposed to solve the low spatial resolution problem but it has a disadvantage of being very sensitive to frequency-band selection. Discriminative filter bank common spatial pattern(DFBCSP) tried to solve the frequency-band selection problem by using the Fisher ratio of the averaged EEG signal power and establishing discriminative filter bank(DFB) which only includes the feature frequency-band. However, we found that DFB might not include the proper filters showing the spatial pattern of ERD. To solve this problem, we apply a band-selection process using CSP feature vectors and linear discriminant analysis to DFBCSP instead of the averaged EEG signal power. The filter selection results and the classification accuracies of the existing and the proposed methods show that the CSP feature is more effective than signal power feature.

• Journal of KIISE
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• v.44 no.6
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• pp.587-594
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• 2017

Recently, motor imagery electroencephalogram(EEG) based Brain-Computer Interface(BCI) systems have received a significant amount of attention in various fields, including medicine and engineering. The Common Spatial Pattern(CSP) algorithm is the most commonly-used method to extract the features from motor imagery EEG. However, the CSP algorithm has limited applicability in Small-Sample Setting(SSS) situations because these situations rely on a covariance matrix. In addition, large differences in performance depend on the frequency bands that are being used. To address these problems, 4-40Hz band EEG signals are divided using nine filter-banks and Regularized CSP(R-CSP) is applied to individual frequency bands. Then, the Mutual Information-Based Individual Feature(MIBIF) algorithm is applied to the features of R-CSP for selecting discriminative features. Thereafter, selected features are used as inputs of the classifier Least Square Support Vector Machine(LS-SVM). The proposed method yielded a classification accuracy of 87.5%, 100%, 63.78%, 82.14%, and 86.11% in five subjects("aa", "al", "av", "aw", and "ay", respectively) for BCI competition III dataset IVa by using 18 channels in the vicinity of the motor area of the cerebral cortex. The proposed method improved the mean classification accuracy by 16.21%, 10.77% and 3.32% compared to the CSP, R-CSP and FBCSP, respectively The proposed method shows a particularly excellent performance in the SSS situation.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.515-520
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• 2012

In brain computer interface (BCI) system, the most important part is classification of human thoughts in order to translate into commands. The more accuracy result in classification the system gets, the more effective BCI system is. To increase the quality of BCI system, we proposed to reduce noise and artifact from the recording data to analyzing data. We used auditory stimuli instead of visual ones to eliminate the eye movement, unwanted visual activation, gaze control. We applied independent component analysis (ICA) algorithm to purify the sources which constructed the raw signals. One of the most famous spatial filter in BCI context is common spatial patterns (CSP), which maximize one class while minimize the other by using covariance matrix. ICA and CSP also do the filter job, as a raw filter and refinement, which increase the classification result of linear discriminant analysis (LDA).

• Proceedings of the International Microelectronics And Packaging Society Conference
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• 2002.05a
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• pp.203-207
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• 2002

CSP(Chip Size Packaging) SAW Filter Package에 대해서, 유한요소해석(Finite Element Analysis) 컴퓨터 Simulation 프로그램인 ANSYS를 이용하여 Package의 온도 분포를 해석하였다. 신뢰성(reliability) Test 조건에서 Transient Thermal Simulation을 한 후, 조건을 변화시켜 가면서 Chip 내부 온도가 어떻게 변화하는지 알아보았다. Chip에 1.8 hour 동안 4W의 열원을 주고, 주위는 2$0^{\circ}C$ 자연대류로 놓고 Transient Thermal Simulation한 결과는 약 99$^{\circ}C$로, 허용 가능한 온도인 11$0^{\circ}C$보다 약 11$^{\circ}C$ 낮음을 알 수 있었다. 또한 이는 실험값인 약 95$^{\circ}C$와 유사한 값을 나타내었다.

• Transactions on Electrical and Electronic Materials
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• v.9 no.1
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• pp.33-37
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• 2008

This paper describes the development of a new $5.0{\times}3.2mm$ SAW filter bank which is consist of 12 L, C matching components and 4 SAW bare chips on PCB substrate with CSP technology. We improved the manufacturing cost by removing the ceramic package through direct flip bonding of $LiTaO_3$ SAW bare chip on PCB board after mounting L, C passive element on PCB board. After that we realized the hermitic sealing by laminating the epoxy film. To confirm the confidentiality and durability of the above method, we have obtained the optimum flip bonding & film laminating condition, and figured out material property and structure to secure the durability & moisture proof of PCB board. The newly developed super mini $5.0{\times}3.2mm$ filter bank shows the superior features than those of existing products in confidence, electrical, mechanical characters.

• Journal of IKEEE
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• v.24 no.2
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• pp.654-660
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• 2020

Through the output of brain waves during muscle operation, this paper checks whether it is possible to find characteristic vectors of brain waves that are capable of dividing left and right movements by extracting brain waves in specific areas of muscle signal output that include the motion of the left and right muscles or the will of the user within EEG signals, where uncertainties exist considerably. A typical surface EMG and noninvasive brain wave extraction method does not exist to distinguish whether the signal is a motion through the degree of ionization by internal neurotransmitter and the magnitude of electrical conductivity. In the case of joint and motor control through normal robot control systems or electrical signals, signals that can be controlled by the transmission and feedback control of specific signals can be identified. However, the human body lacks evidence to find the exact protocols between the brain and the muscles. Therefore, in this paper, efficiency is verified by utilizing the results of application of CSP (Common Spatial Pattern) filter to verify that the left-hand and right-hand signals can be extracted through brainwave analysis when the subject's behavior is performed. In addition, we propose ways to obtain data through experimental design for verification, to verify the change in results with or without filter application, and to increase the accuracy of the classification.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.11 s.353
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• pp.8-13
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• 2006

Recent RF SAW filters are made using a HTCC package with a CSP(chip scale Package) technology. This paper describes a development of a new $1.4{\times}1.1\;and\;2.0{\times}1.4mm$ RF SAW liters made by PCB substrate instead of HTCC package, and this technology can reduce the cost of materials down to 40%. We have investigated the multi-layered PCB substrate structures and raw materials to find out the optimal flip-bonding condition between the $LiTaO_3$ wafer and PCB substrates. Also the optimal materials and processing conditions of epoxy laminating film were found out through the experiments which can reduce the bending moment caused by the difference of the thermal expansion between the PCB substrate and laminating film. The new PCB SAW filter shows good electrical and reliability performances with respect to the present SAW filters.

• Geophysics and Geophysical Exploration
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• v.19 no.1
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• pp.11-19
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• 2016

Two representative residual static methods of traveltime decomposition and stack-power maximization are discussed in terms of application to land seismic data. For the model data with synthetic shot/receiver statics (time shift) applied and random noises added, continuities of reflection event are much improved by stack-power maximization method, resulting the derived time-shifts approximately equal to the synthetic statics. Optimal parameters (maximum allowable shift, correlation window, iteration number) for residual statics are effectively chosen with diagnostic displays of CSP (common shot point) stack and CRP (common receiver point) stack as well as CMP gather. In addition to removal of long-wavelength time shift by refraction statics, prior to residual statics, processing steps of f-k filter, predictive deconvolution and time variant spectral whitening are employed to attenuate noises and thereby to minimize the error during the correlation process. The reflectors including horizontal layer of reservoir are more clearly shown in the variable-density section through repicking the velocities after residual statics and inverse NMO correction.