Ceph is an object-based parallel distributed file system that provides excellent performance, reliability, and scalability. Additionally, Ceph provides its Cephx authentication system to authenticate users, so that it can identify users and realize authentication. In this paper, we first model the basic architecture of Ceph using process algebra CSP (Communicating Sequential Processes). With the help of the model checker PAT (Process Analysis Toolkit), we feed the constructed model to PAT and then verify several related properties, including Deadlock Freedom, Data Reachability, Data Write Integrity, Data Consistency and Authentication. The verification results show that the original model cannot cater to the Authentication property. Therefore, we formalize a new model of Ceph where Cephx is adopted. In the light of the new verification results, it can be found that Cephx satisfies all these properties.

Accurate and automatic quantitative cephalometry analysis is of great importance in orthodontics. The fundamental step for cephalometry analysis is to annotate anatomic-interested landmarks on X-ray images. Computer-aided automatic method remains to be an open topic nowadays. In this paper, we propose an efficient deep learning-based coarse-to-fine approach to realize accurate landmark detection. In the coarse detection step, we train a deep learning-based deformable transformation model by using training samples. We register test images to the reference image (one training image) using the trained model to predict coarse landmarks' locations on test images. Thus, regions of interest (ROIs) which include landmarks can be located. In the fine detection step, we utilize trained deep convolutional neural networks (CNNs), to detect landmarks in ROI patches. For each landmark, there is one corresponding neural network, which directly does regression to the landmark's coordinates. The fine step can be considered as a refinement or fine-tuning step based on the coarse detection step. We validated the proposed method on public dataset from 2015 International Symposium on Biomedical Imaging (ISBI) grand challenge. Compared with the state-of-the-art method, we not only achieved the comparable detection accuracy (the mean radial error is about 1.0-1.6mm), but also largely shortened the computation time (4 seconds per image).

In this study we propose a method to perform personal identification (PI) based on Electroencephalogram (EEG) signals, where the used network is named residual and multiscale spatio-temporal convolution neural network (RAMST-CNN). Combined with some popular techniques in deep learning, including residual learning (RL), multi-scale grouping convolution (MGC), global average pooling (GAP) and batch normalization (BN), RAMST-CNN has powerful spatio-temporal feature extraction ability as it achieves task-independence that avoids the complexity of selecting and extracting features manually. Experiments were carried out on multiple datasets, the results of which were compared with methods from other studies. The results show that the proposed method has a higher recognition accuracy even though the network it is based on is lightweight.

Brain-computer interfaces (BCIs) have been used by users to convey their intentions directly with brain signals. For example, a spelling system that uses EEGs allows letters on a display to be selected. In comparison, previous studies have investigated decoding speech information such as syllables, words from single-trial brain signals during speech comprehension, or articulatory imagination. Such decoding realizes speech recognition with a relatively short time-lag and without relying on a display. Previous magnetoencephalogram (MEG) research showed that a template matching method could be used to classify three English sentences by using phase patterns in theta oscillations. This method is based on the synchronization between speech rhythms and neural oscillations during speech processing, that is, theta oscillations synchronized with syllabic rhythms and low-gamma oscillations with phonemic rhythms. The present study aimed to approximate this classification method to a BCI application. To this end, (1) we investigated the performance of the EEG-based classification of three Japanese sentences and (2) evaluated the generalizability of our models to other different users. For the purpose of improving accuracy, (3) we investigated the performances of four classifiers: template matching (baseline), logistic regression, support vector machine, and random forest. In addition, (4) we propose using novel features including phase patterns in a higher frequency range. Our proposed features were constructed in order to capture synchronization in a low-gamma band, that is, (i) phases in EEG oscillations in the range of 2-50 Hz from all electrodes used for measuring EEG data (all) and (ii) phases selected on the basis of feature importance (selected). The classification results showed that, except for random forest, most classifiers perform similarly. Our proposed features improved the classification accuracy with statistical significance compared with a baseline feature, which is a phase pattern in neural oscillations in the range of 4-8 Hz from the right hemisphere. The best mean accuracy across folds was 55.9% using template matching trained by all features. We concluded that the use of phase information in a higher frequency band improves the performance of EEG-based sentence classification and that this model is applicable to other different users.

A study on quantification of human stress using low beta waves of electroencephalography (EEG) is presented. For the very first time the importance of low beta waves as a feature for quantification of human stress is highlighted. In this study, there were twenty-eight participants who filled the Perceived Stress Scale (PSS) questionnaire and recorded their EEG in closed eye condition by using a commercially available single channel EEG headset placed at frontal site. On the regression analysis of beta waves extracted from recorded EEG, it has been observed that low beta waves can predict PSS scores with a confidence level of 94%. Consequently, when low beta wave is used as a feature with the Naive Bayes algorithm for classification of stress level, it not only reduces the computational cost by 7 folds but also improves the accuracy to 71.4%.

In this paper a new method for noise removal from single-trial event-related potentials recorded with a multi-channel electroencephalogram is addressed. An observed signal is separated into multiple signals with a multi-channel Wiener filter whose coefficients are estimated based on parameter estimation of a probabilistic generative model that locally models the amplitude of each separated signal in the time-frequency domain. Effectiveness of using prior information about covariance matrices to estimate model parameters and frequency dependent covariance matrices were shown through an experiment with a simulated event-related potential data set.

Research on emotion recognition using electroencephalogram (EEG) of subjects listening to music has become more active in the past decade. However, previous works did not consider emotional oscillations within a single musical piece. In this research, we propose a continuous music-emotion recognition approach based on brainwave signals. While considering the subject-dependent and changing-over-time characteristics of emotion, our experiment included self-reporting and continuous emotion annotation in the arousal-valence space. Fractal dimension (FD) and power spectral density (PSD) approaches were adopted to extract informative features from raw EEG signals and then we applied emotion classification algorithms to discriminate binary classes of emotion. According to our experimental results, FD slightly outperformed PSD approach both in arousal and valence classification, and FD was found to have the higher correlation with emotion reports than PSD. In addition, continuous emotion recognition during music listening based on EEG was found to be an effective method for tracking emotional reporting oscillations and provides an opportunity to better understand human emotional processes.

As sensitive magnetic sensors, magnetometers based on superconducting quantum interference devices can be used for the detection of weak magnetic fields. These signals can be generated by diverse origins, for example, brain electric activity, myocardium electric activity, and nuclear precession of hydrogen protons. In addition, weak current induced in the low-temperature detectors, for example, transition-edge sensors can be detected using SQUIDs. And, change of magnetic flux quantum generated in a superconducting ring can be detected by SQUID, which can be used for realization of mechanical force. Thus, SQUIDs are key elements in precision metrology. In Korea, development of low-temperature SQUIDs based on Nb-Josephson junctions was started in late 1980s, and Nb-based SQUIDs have been used mainly for biomagnetic measurements; magnetocardiography and magnetoencephalography. High-Tc SQUIDs, being developed in mid 1990s, were used for magnetocardiography and nondestructive evaluation. Recently, SQUID-based low-field nuclear magnetic resonance technology is under development. In this paper, we review the past progress and recent activity of SQUID applications in Korea, with focus on biomagnetic measurements.

Magnetoencephalography (MEG) measures very weak neuromagnetic signals using SQUID sensors. Standard MEG analyses include averaged waveforms, isofield maps and equivalent current dipoles. Beamforming MEG analyses provide us with frequency-dependent spatiotemporal information about the cerebral oscillatory changes related to not only somatosensory processing but also language processing. Language dominance is able to be evaluated using laterality of power attenuation in the low γ band in the frontal area. Neuromagnetic signals of the unilateral upper movements are able to be decoded using a support vector machine.

We developed a novel movement-imagery-based brain-computer interface (BCI) for untrained subjects without employing machine learning techniques. The development of BCI consisted of several steps. First, spline Laplacian analysis was performed. Next, time-frequency analysis was applied to determine the optimal frequency range and latencies of the electroencephalograms (EEGs). Finally, trials were classified as right or left based on β-band event-related synchronization using the cumulative distribution function of pretrigger EEG noise. To test the performance of the BCI, EEGs during the execution and imagination of right/left wrist-bending movements were measured from 63 locations over the entire scalp using eight healthy subjects. The highest classification accuracies were 84.4% and 77.8% for real movements and their imageries, respectively. The accuracy is significantly higher than that of previously reported machine-learning-based BCIs in the movement imagery task (paired t-test, p < 0.05). It has also been demonstrated that the highest accuracy was achieved even though subjects had never participated in movement imageries.

Many studies of on-line comprehension of semantic violations have shown that the human sentence processor rapidly constructs a higher-order semantic interpretation of the sentence. What remains unclear, however, is the amount of time required to detect semantic anomalies while concatenating two words to form a phrase with very rapid stimuli presentation. We aimed to examine the time course of semantic integration in concatenating two words in phrase structure building, using magnetoencephalography (MEG). In the MEG experiment, subjects decided whether two words (a classifier and its corresponding noun), presented each for 66 ms, form a semantically correct noun phrase. Half of the stimuli were matched pairs of classifiers and nouns. The other half were mismatched pairs of classifiers and nouns. In the analysis of MEG data, there were three primary peaks found at approximately 25 ms (M1), 170 ms (M2) and 250 ms (M3) after the presentation of the target words. As a result, only the M3 latencies were significantly affected by the stimulus conditions. Thus, the present results indicate that the semantic integration in concatenating two words starts from approximately 250 ms.

In this study, we propose a method of classifying a spontaneous electroencephalogram (EEG) approach to a brain-computer interface. Ten subjects, aged 21-32 years, volunteered to imagine left- and right-hand movements. An independent component analysis based on a fixed-point algorithm is used to eliminate the activities found in the EEG signals. We use a fractal dimension value to reveal the embedded potential responses in the human brain. The different fractal dimension values between the relaxing and imaging periods are computed. Featured data is classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Two conventional methods, namely, the use of the autoregressive (AR) model and the band power estimation (BPE) as features, and the linear discriminant analysis (LDA) as a classifier, are selected for comparison in this study. Experimental results show that the proposed method is more effective than the conventional methods.

Magnetoencephalography (MEG) is a method to measure a magnetic field generated by electrical neural activity in a brain, and it plays increasingly important role in clinical diagnoses and neurophysiological studies. However, in MEG analysis, the estimation of the brain activity, of the electric current density distribution in a brain which is represented by current dipoles, is problematic. A spatial filter and subsequent reconstruction of the current density distribution estimated by the spatial filter (spatial filtered reconstruction: SFR) are proposed. The spatial filter is designed to be used without prior or temporal information. The proposed spatial filter ensures that it concentrates the current distribution around the activated sources in the conductor. The current distribution estimated by the spatial filter is reconstructed by multiple linear regression. Redundant current dipoles are eliminated, and the current distribution is optimized in the sense of the Mallows Cp statistic. Numerical studies are demonstrated and show successful estimation by SFR in multiple-dipole cases. In single-dipole cases with SNRs of 101 and more, the location of the true dipole was successfully estimated for about 80% of the simulations. The reconstruction with multiple linear regression corrected the location of the maximum current density estimated by the proposed spatial filtering. The dipole on the correct position contributes to more than 70% of the total dipoles in the estimated current distribution in those cases. These results show that the current distribution is effectively localized by SFR. We also investigate the differences among SFR, the LCMV (linearly constrained minimum variance) beamformer and the SAM (synthetic aperture magnetometry), the representatives of spatial filters in MEG analyses. It is indicated that spatial resolution is improved by avoiding dependence on temporal information.

Multichannel superconducting quantum interference device (SQUID) systems based on double relaxation oscillation SQUIDs (DROS) were developed for measuring magnetocardiography (MCG) and magnetoencephalography (MEG) signals. Since DROS provides large flux-to-voltage transfer coefficients, about 10 times larger than the DC SQUIDs, direct readout of the SQUID output was possible using compact room-temperature electronics. Using DROSs, we fabricated two types of multichannel systems; a 37-channel magnetometer system with circular sensor distribution for measuring radial components of MEG signals, and two planar gradiometer systems of 40-channel and 62-channel measuring tangential components of MCG or MEG signals. The magnetometer system has external feedback to eliminate magnetic coupling with adjacent channels, and reference vector magnetometers were installed to form software gradiometers. The field noise of the magnetometers is around 3 fT/ at 100 Hz inside a magnetically shielded room. The planar gradiometer systems have integrated first-order gradiometer in thin-film form with a baseline of 40 mm. The magnetic field gradient noise of the planar gradiometers is about 1 fT/cm/ at 100 Hz. The planar gradiometers were arranged to measure field components tangential to the body surface, providing efficient measurement of especially MCG signals with smaller sensor coverage than the conventional normal component measurements.

A brain-computer interface using an electroencephalogram as input into an artificial neural network is investigated as a potentially general control system applicable to all subjects and time frames. Using the intent and imagination of bending the left or right elbow, the left and right desired movements are successfully distinguished using event-related desynchronization resolved by fast Fourier transformation of the electroencephalogram and analysis of the power spectrum using the artificial neural network. The influence of age was identified and eliminated through the use of a frequency distribution in the α band, and the recognition rate was further improved by confirmation based on forced excitement of the β band in the case of an error. The proposed system was effectively trained for general use by using the combined data of a cross-section of subjects.

Treating an averaged multiple-trials data or non-averaged single-trial data is a main approach in recent topics on applying independent component analysis (ICA) to neurobiological signal processing. By taking an average, the signal-to-noise ratio (SNR) is increased but some important information such as the strength of an evoked response and its dynamics will be lost. The single-trial data analysis, on the other hand, can avoid this problem but the SNR is very poor. In this study, we apply ICA to both non-averaged single-trial data and averaged multiple-trials data to determine the properties and advantages of both. Our results show that the analysis of averaged data is effective for seeking the response and dipole location of evoked fields. The non-averaged single-trial data analysis efficiently identifies the strength and dynamic component such as α-wave. For determining both the range of evoked strength and dipole location, an analysis of averaged limited-trials data is better option.

Magnetoencephalography (MEG) is a powerful and non-invasive technique for measuring human brain activity with a high temporal resolution. The motivation for studying MEG data analysis is to extract the essential features from measured data and represent them corresponding to the human brain functions. In this paper, a novel MEG data analysis method based on independent component analysis (ICA) approach with pre-processing and post-processing multistage procedures is proposed. Moreover, several kinds of ICA algorithms are investigated for analyzing MEG single-trial data which is recorded in the experiment of phantom. The analyzed results are presented to illustrate the effectiveness and high performance both in source decomposition by ICA approaches and source localization by equivalent current dipoles fitting method.

Analysis of electroencephalogram (EEG) is presented for sleep physiology. This analysis is performed by the Instantaneous Maximum Entropy Method (IMEM), which was given by the author. Appearance and continuation of featuristic waves are not steady in EEG. The characteristics of these waves responding to epoch of sleep are analyzed. The behaviours of waves were clarified by this analysis as follows; (a) time dependent frequency of continuous oscillations of alpha rhythm was observed precisely. Sleep spindles were detected clearly within NREM and these parameters of time, frequency, and peak energy were specified. (b) delta waves with very low frequencies and sleep spindles were observed simultaneously. And (c) the relationship of sleep spindles and delta waves was first detected with negative correlation along time-axis. The analysis by the IMEM was found effective comparing conventional analysis method of FFT, bandpass filter bank, etc.

This letter shows that a portable visual stimulator for MEG measurements can be realized using an optical fiber bundle and a CRT display system offering high brightness and high speed raster scanning, and that MEGs with neither magnetic contamination nor jitter can be measured by the stimulator.