EEG-based video identification using graph signal modeling and graph convolutional neural network

This paper proposes a novel graph signal-based deep learning method for electroencephalography (EEG) and its application to EEG-based video identification. We present new methods to effectively represent EEG data as signals on graphs, and learn them using graph convolutional neural networks. Experimental results for video identification using EEG responses obtained while watching videos show the effectiveness of the proposed approach in comparison to existing methods. Effective schemes for graph signal representation of EEG are also discussed.Comment: Accepted and presented at ICASSP 201

Evaluation of Preference of Multimedia Content using Deep Neural Networks for Electroencephalography

Evaluation of quality of experience (QoE) based on electroencephalography (EEG) has received great attention due to its capability of real-time QoE monitoring of users. However, it still suffers from rather low recognition accuracy. In this paper, we propose a novel method using deep neural networks toward improved modeling of EEG and thereby improved recognition accuracy. In particular, we aim to model spatio-temporal characteristics relevant for QoE analysis within learning models. The results demonstrate the effectiveness of the proposed method.Comment: Accepted for the 10th International Conference on Quality of Multimedia Experience (QoMEX 2018

A Study of Environmental Effects on Galaxy Spin Using MaNGA Data

We investigate environmental effects on galaxy spin using the recent public data of MaNGA integral field spectroscopic survey containing ~2800 galaxies. We measure the spin parameter of 1830 galaxies through the analysis of two-dimensional stellar kinematic maps within the effective radii, and obtain their large- (background mass density from 20 nearby galaxies) and small-scale (distance to and morphology of the nearest neighbour galaxy) environmental parameters for 1529 and 1767 galaxies, respectively. We first examine the mass dependence of galaxy spin, and find that the spin parameter of early-type galaxies decreases with stellar mass at log (M$_*/$M$_{\odot}$) $\gtrsim$ 10, consistent with the results from previous studies. We then divide the galaxies into three subsamples using their stellar masses to minimize the mass effects on galaxy spin. The spin parameters of galaxies in each subsample do not change with background mass density, but do change with distance to and morphology of the nearest neighbour. In particular, the spin parameter of late-type galaxies decreases as early-type neighbours approach within the virial radius. These results suggest that the large-scale environments hardly affect the galaxy spin, but the small-scale environments such as hydrodynamic galaxy-galaxy interactions can play a substantial role in determining galaxy spin.Comment: 12 pages, 11 figures, Accepted for publication in MNRA

Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information

Emotion recognition based on electroencephalography (EEG) has received attention as a way to implement human-centric services. However, there is still much room for improvement, particularly in terms of the recognition accuracy. In this paper, we propose a novel deep learning approach using convolutional neural networks (CNNs) for EEG-based emotion recognition. In particular, we employ brain connectivity features that have not been used with deep learning models in previous studies, which can account for synchronous activations of different brain regions. In addition, we develop a method to effectively capture asymmetric brain activity patterns that are important for emotion recognition. Experimental results confirm the effectiveness of our approach.Comment: Accepted for the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018

The Calibration of Star Formation Rate Indicators for WISE 22 Micron Selected Galaxies in the SDSS

We study star formation rate (SFR) indicators for Wide-field Infrared Survey Explorer (WISE) 22 \mu m selected, star-forming galaxies at 0.01 < z < 0.3 in the Sloan Digital Sky Survey. Using extinction-corrected H\alpha\ luminosities and total infrared luminosities as reference SFR estimates, we calibrate WISE mid-infrared (MIR) related SFR indicators. Both 12 and 22 \mu m monochromatic luminosities correlate well with the reference SFR estimates, but tend to underestimate SFRs of metal-poor galaxies (at lower than solar metallicity), consistent with previous studies. We mitigate this metallicity dependence using a linear combination of observed H\alpha\ and WISE MIR luminosities for SFR estimates. The combination provides robust SFR measurements as Kennicutt et al. (2009) applied to Spitzer data. However, we find that the coefficient a in L_H\alpha(obs) + a L_MIR increases with SFR, and show that a non-linear combination of observed H\alpha\ and MIR luminosities gives the best SFR estimates with small scatters and with little dependence on physical parameters. Such a combination of H\alpha\ and MIR luminosities for SFR estimates is first applied to WISE data. We provide several SFR recipes using WISE data applicable to galaxies with 0.1 <~ SFR (M_sun yr^-1) <~ 100.Comment: 14 pages, 2 tables, 6 figures, to appear in Ap

Do Bars Trigger Activity in Galactic Nuclei?

We investigate the connection between the presence of bars and AGN activity, using a volume-limited sample of $\sim$9,000 late-type galaxies with axis ratio $b/a>0.6$ and $M_{r} < -19.5+5{\rm log}h$ at low redshift ($0.02\le z\lesssim 0.055$), selected from Sloan Digital Sky Survey Data Release 7. We find that the bar fraction in AGN-host galaxies (42.6%) is $\sim$2.5 times higher than in non-AGN galaxies (15.6%), and that the AGN fraction is a factor of two higher in strong-barred galaxies (34.5%) than in non-barred galaxies (15.0%). However, these trends are simply caused by the fact that AGN-host galaxies are on average more massive and redder than non-AGN galaxies because the fraction of strong-barred galaxies (\bfrsbo) increases with $u-r$ color and stellar velocity dispersion. When $u-r$ color and velocity dispersion (or stellar mass) are fixed, both the excess of \bfrsbo in AGN-host galaxies and the enhanced AGN fraction in strong-barred galaxies disappears. Among AGN-host galaxies we find no strong difference of the Eddington ratio distributions between barred and non-barred systems. These results indicate that AGN activity is not dominated by the presence of bars, and that AGN power is not enhanced by bars. In conclusion we do not find a clear evidence that bars trigger AGN activity.Comment: 13 pages, 11 figures, accepted for publication in Ap

Stellar Populations of Early-type Galaxies with Mid-Infrared Excess Emission

We present a stellar population analysis of quiescent (without H$\alpha$ emission) and bright ($M_{r}$ $<$ $-$21.5) early-type galaxies (ETGs) with recent star formation. The ETGs are selected from a spectroscopic sample of SDSS galaxies at 0.04 $<$ $z$ $<$ 0.11 with {\it WISE} mid-infrared (IR) and {\it GALEX} near-ultraviolet (UV) emissions. We stack the optical spectra of ETGs with different amounts of mid-IR and near-UV excess emissions to measure the strength of 4000 \AA{} break $D_{n}$4000 and the width of Balmer absorption line H$\delta_{A}$ that are indicative of recent ($\sim$1 Gyr) star formation activity. The {\it WISE} [3.4]$-$[12] colors show stronger correlations with the spectral features than NUV$-r$ colors. We fit to the stacked spectra with a spectral fitting code, STARLIGHT, and find that the mass fraction of young ($\leq$1 Gyr) and intermediate-age ($\sim$1$-$5 Gyr) stars in the ETGs with mid-IR excess emission is $\sim$4$-$11\%, depending on the template spectrum used for the fit. These results show that the ETGs with mid-IR excess emission have experienced star formation within the last 1$-$5 Gyr and that the mid-IR emission is a useful diagnostic tool for probing recent star formation activity in ETGs.Comment: 15 pages, 7 figures, accepted for publication in Ap

Demise of Faint Satellites around Isolated Early-type Galaxies

The hierarchical galaxy formation scenario in the Cold Dark Matter cosmogony with a non-vanishing cosmological constant and geometrically flat space has been very successful in explaining the large-scale distribution of galaxies. However, there have been claims that the scenario predicts too many satellite galaxies associated with massive galaxies compared to observations, called the missing satellite galaxy problem. Isolated groups of galaxies hosted by passively evolving massive early-type galaxies are ideal laboratories for finding the missing physics in the current theory. Here we report from a deep spectroscopic survey of such satellite systems that isolated massive early-type galaxies with no recent star formation through wet mergers or accretion have almost no satellite galaxies fainter than the r-band absolute magnitude of about Mr =-14. If only early-type satellites are used, the cutoff is at somewhat brighter magnitude of about Mr =-15. Such a cutoff has not been found in other nearby satellite galaxy systems hosted by late-type galaxies or those with merger features. Various physical properties of satellites depend strongly on the host-centric distance. Our observation indicates that the satellite galaxy luminosity function is largely determined by the interaction of satellites with the environment provided by their host, which sheds light on the missing satellite galaxy problem.Comment: 10 pages, 3 figures (main); 12 pages, 4 figures (supplementary); accepted for publication in Nature Astronom

Deep Learning Based NLOS Identification with Commodity WLAN Devices

Identifying line-of-sight (LOS) and non-LOS (NLOS) channel conditions can improve the performance of many wireless applications, such as signal strength-based localization algorithms. For this purpose, channel state information (CSI) obtained by commodity IEEE 802.11n devices can be used, because it contains information about channel impulse response (CIR). However, because of the limited sampling rate of the devices, a high-resolution CIR is not available, and it is difficult to detect the existence of an LOS path from a single CSI measurement, but it can be inferred from the variation pattern of CSI over time. To this end, we propose a recurrent neural network (RNN) model, which takes a series of CSI to identify the corresponding channel condition. We collect numerous measurement data under an indoor office environment, train the proposed RNN model, and compare the performance with those of existing schemes that use handcrafted features. The proposed method efficiently learns a non-linear relationship between input and output, and thus, yields high accuracy even for data obtained in a very short period.Comment: 9 pages, 9 figures, Accepted for publication in IEEE Transactions on Vehicular Technolog