• Investigative Magnetic Resonance Imaging
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• v.21 no.4
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• pp.223-232
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• 2017

Purpose: To report the use of multiband accelerated echo-planar imaging (EPI) for resting-state functional MRI (rs-fMRI) to achieve rapid high temporal resolution at 3T compared to conventional EPI. Materials and Methods: rs-fMRI data were acquired from 20 healthy right-handed volunteers by using three methods: conventional single-band gradient-echo EPI acquisition (Data 1), multiband gradient-echo EPI acquisition with 240 volumes (Data 2) and 480 volumes (Data 3). Temporal signal-to-noise ratio (tSNR) maps were obtained by dividing the mean of the time course of each voxel by its temporal standard deviation. The resting-state sensorimotor network (SMN) and default mode network (DMN) were estimated using independent component analysis (ICA) and a seed-based method. One-way analysis of variance (ANOVA) was performed between the tSNR map, SMN, and DMN from the three data sets for between-group analysis. P < 0.05 with a family-wise error (FWE) correction for multiple comparisons was considered statistically significant. Results: One-way ANOVA and post-hoc two-sample t-tests showed that the tSNR was higher in Data 1 than Data 2 and 3 in white matter structures such as the striatum and medial and superior longitudinal fasciculus. One-way ANOVA revealed no differences in SMN or DMN across the three data sets. Conclusion: Within the adapted metrics estimated under specific imaging conditions employed in this study, multiband accelerated EPI, which substantially reduced scan times, provides the same quality image of functional connectivity as rs-fMRI by using conventional EPI at 3T. Under employed imaging conditions, this technique shows strong potential for clinical acceptance and translation of rs-fMRI protocols with potential advantages in spatial and/or temporal resolution. However, further study is warranted to evaluate whether the current findings can be generalized in diverse settings.

• Journal of Internet Computing and Services
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• v.20 no.5
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• pp.27-36
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• 2019

In this study, experiments on the improvement of the emotion classification, analysis and accuracy of EEG data were proceeded, which applied DEAP (a Database for Emotion Analysis using Physiological signals) dataset. In the experiment, total 32 of EEG channel data measured from 32 of subjects were applied. In pre-processing step, 256Hz sampling tasks of the EEG data were conducted, each wave range of the frequency (Hz); Theta, Slow-alpha, Alpha, Beta and Gamma were then extracted by using Finite Impulse Response Filter. After the extracted data were classified through Time-frequency transform, the data were purified through Independent Component Analysis to delete artifacts. The purified data were converted into CSV file format in order to conduct experiments of Machine learning algorithm and Arousal-Valence plane was used in the criteria of the emotion classification. The emotions were categorized into three-sections; 'Positive', 'Negative' and 'Neutral' meaning the tranquil (neutral) emotional condition. Data of 'Neutral' condition were classified by using Cz(Central zero) channel configured as Reference channel. To enhance the accuracy ratio, the experiment was performed by applying the attributes selected by ASC(Attribute Selected Classifier). In "Arousal" sector, the accuracy of this study's experiments was higher at "32.48%" than Koelstra's results. And the result of ASC showed higher accuracy at "8.13%" compare to the Liu's results in "Valence". In the experiment of Random Forest Classifier adapting ASC to improve accuracy, the higher accuracy rate at "2.68%" was confirmed than Total mean as the criterion compare to the existing researches.

• Progress in Superconductivity
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• v.7 no.1
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• pp.28-35
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• 2005

We propose a method to measure atrial arrhythmias (AA) such as atrial fibrillation (Afb) and atrial flutter (Afl) with a SQUID magnetocardiograph (MCG) system. To detect AA is one of challenging topics in MCG. As the AA generally have irregular rhythm and atrio-ventricular conduction, the MCG signal cannot be improved by QRS averaging; therefore a SQUID MCG system having a high SNR is required to measure informative atrial excitation with a single scan. In the case of Afb, diminished f waves are much smaller than normal P waves because the sources are usually located on the posterior wall of the heart. In this study, we utilize an MCG system measuring tangential field components, which is known to be more sensitive to a deeper current source. The average noise spectral density of the whole system in a magnetic shielded room was $10\;fT/{\surd}Hz(a)\;1\;Hz\;and\;5\;fT/{\surd}Hz\;(a)\;100\;Hz$. We measured the MCG signals of patients with chronic Afb and Afl. Before the AA measurement, the comparison between the measurements in supine and prone positions for P waves has been conducted and the experiment gave a result that the supine position is more suitable to measure the atrial excitation. Therefore, the AA was measured in subject's supine position. Clinical potential of AA measurement in MCG is to find an aspect of a reentry circuit and to localize the abnormal stimulation noninvasively. To give useful information about the abnormal excitation, we have developed a method, separative synthetic aperture magnetometry (sSAM). The basic idea of sSAM is to visualize current source distribution corresponding to the atrial excitation, which are separated from the ventricular excitation and the Gaussian sensor noises. By using sSAM, we localized the source of an Afl successfully.

• Journal of Broadcast Engineering
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• v.21 no.2
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• pp.169-179
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• 2016

Recently, MPEG-H has been standardizing for a multimedia coder in UHDTV (Ultra-High-Definition TV). Thus, the demand for not only channel-based audio contents but also object-based audio contents is more increasing, which results in developing a new technique of converting channel-based audio contents to object-based ones. In this paper, a non-uniform linear microphone array based source separation method is proposed for realizing such conversion. The proposed method first analyzes the arrival time differences of input audio sources to each of the microphones, and the spectral magnitudes of each sound source are estimated at the horizontal directions based on the analyzed time differences. In order to demonstrate the effectiveness of the proposed method, objective performance measures of the proposed method are compared with those of conventional methods such as an MVDR (Minimum Variance Distortionless Response) beamformer and an ICA (Independent Component Analysis) method. As a result, it is shown that the proposed separation method has better separation performance than the conventional separation methods.