Complexity growth rates for AdS black holes in massive gravity and f(R)f(R) gravity

The "complexity = action" duality states that the quantum complexity is equal to the action of the stationary AdS black holes within the Wheeler-DeWitt patch at late time approximation. We compute the action growth rates of the neutral and charged black holes in massive gravity and the neutral, charged and Kerr-Newman black holes in $f(R)$ gravity to test this conjecture. Besides, we investigate the effects of the massive graviton terms, higher derivative terms and the topology of the black hole horizon on the complexity growth rate.Comment: 11 pages, no figur

Evaluating functional connectivity in alcoholics based on maximal weight matching

EEG-based applications have faced the challenge of multi-modal integrated analysis problems. In this paper, a greedy maximal weight matching approach is used to measure the functional connectivity in alcoholics datasets with EEG and EOG signals. The major discovery is that the processing of the repeated and unrepeated stimuli in the γ band in control drinkers is significantly more different than that in alcoholic subjects. However, the EOGs are always stable in the case of visual tasks, except for a weakly wave when subjects make an error response to the stimul

Simulation α of EEG using brain network model

In this paper, we developed a large-scale brain network model comprising of four cerebral areas in the left hemisphere, and each area is modelled as an oscillator Jansen and Rit (JR) model. Our model is based on the structural connectivity of human connectome (SC) which was a hybrid from CoCoMac neuroinformatics database and diffusion spectrum imaging (DSI.) This brain network model was designed and implemented on the neuroinformatics platform using The Virtual Brain (TVB v1.5.3). The results demonstrated that incorporating the large-scale connectivity of brain regions and neural mass of JR model can generate signals similar to the α oscillation in frequency range of (7-12HZ) of EEG

An improved chaos method for monitoring the depth of anaesthesia

This paper proposed a new method to monitor the depth of anaesthesia (DoA) by modifying the Hurst parameters in Chaos method. Two new indices (CDoA and CsDoA) are proposed to estimate the anaesthesia states of patients. In order to reduce the fluctuation of CDoA and CsDoA trends, the Chaos and Modified Detrended Average methods (C-MDMA) are combined together. Compared with Bispectrum (BIS) index, CDoA, the CsDoA and C-MDMA trends are close to the BIS trend in the whole scale from 100 to 0 with a full recording time

Chaos-modified detrended moving average methodology for monitoring the depth of anaesthesia

This paper proposes a new method to monitor the depth of anaesthesia (DoA) based on the EEG signal. This approach firstly uses discrete wavelet transform (DWT) to to remove the spikes and the low frequency noise from raw EEG signals. After de-noising the EEG signals, the modified Hurst parameter is proposed with two new indices (CDoA and CsDoA), to estimate the anaesthesia states of the patients. To reduce the fluctuation of the new DoA index, a combination of Modified Chaos and Modifying Detrended Moving Average is used (MC-DMA). Analyses of variance (ANOVA) for C-MDMA and BIS distributions are presented The results indicate that the C-MDMA distributions at each anaesthesia state level are significantly different and the C-MDMA can distinguish five depths of anaesthesia. Compared with BIS trends, MC-DMA trend is close to BIS trend covering the whole scale from 100 to 0 with a full recording time