Wavelet-Based Biphase Analysis of Brain Rhythms in Automated Wake–Sleep Classification

Many studies in the field of sleep have focused on connectivity and coherence. Still, the nonstationary nature of electroencephalography (EEG) makes many of the previous methods unsuitable for automatic sleep detection. Time-frequency representations and high-order spectra are applied to nonstationary signal analysis and nonlinearity investigation, respectively. Therefore, combining wavelet and bispectrum, wavelet-based bi-phase (Wbiph) was proposed and used as a novel feature for sleep–wake classification. The results of the statistical analysis with emphasis on the importance of the gamma rhythm in sleep detection show that the Wbiph is more potent than coherence in the wake–sleep classification. The Wbiph has not been used in sleep studies before. However, the results and inherent advantages, such as the use of wavelet and bispectrum in its definition, suggest it as an excellent alternative to coherence. In the next part of this paper, a convolutional neural network (CNN) classifier was applied for the sleep–wake classification by Wbiph. The classification accuracy was 97.17% in nonLOSO and 95.48% in LOSO cross-validation, which is the best among previous studies on sleep–wake classification.

Method, models and information technology of decision making about the state of technical objects under non-stationary input influences

There is a significant increase in the amount of measuring information at complex and large technical objects, such as bridges. Decision making about these objects states under non-stationary input influences is a difficult task. The article proposes to make the transition from single-channel information processing to multi-channel one. Each channel processes one of the Hilbert-Huang modes, into which each realization of the nonstationary signal decomposes. It is shown that the first three modes of decomposition are often enough, which in most cases create a stationary process. If a mode is non-stationary, it is possible to decompose it into these modes. The final decision according to statistical criteria is made not on realizations as it is traditionally carried out, but on Hilbert-Huang modes.

Application of Empirical Mode Decomposition for Decoding Perception of Faces Using Magnetoencephalography

Neural decoding is useful to explore the timing and source location in which the brain encodes information. Higher classification accuracy means that an analysis is more likely to succeed in extracting useful information from noises. In this paper, we present the application of a nonlinear, nonstationary signal decomposition technique—the empirical mode decomposition (EMD), on MEG data. We discuss the fundamental concepts and importance of nonlinear methods when it comes to analyzing brainwave signals and demonstrate the procedure on a set of open-source MEG facial recognition task dataset. The improved clarity of data allowed further decoding analysis to capture distinguishing features between conditions that were formerly over-looked in the existing literature, while raising interesting questions concerning hemispheric dominance to the encoding process of facial and identity information.

Nonstationary signal extraction based on BatOMP sparse decomposition technique

Sparse decomposition technique is a new method for nonstationary signal extraction in a noise background. To solve the problem of accuracy and efficiency exclusive in sparse decomposition, the bat algorithm combined with Orthogonal Matching Pursuits (BatOMP) was proposed to improve sparse decomposition, which can realize adaptive recognition and extraction of nonstationary signal containing random noise. Two general atoms were designed for typical signals, and dictionary training method based on correlation detection and Hilbert transform was developed. The sparse decomposition was turned into an optimizing problem by introducing bat algorithm with optimized fitness function. By contrast with several relevant methods, it was indicated that BatOMP can improve convergence speed and extraction accuracy efficiently as well as decrease the hardware requirement, which is cost effective and helps broadening the applications.

Nonstationary Signal Extraction Based on Bat Algorithm Sparse Decomposition Technique

For nonstationary time series i.e. natural electromagnetic field and acoustical signal, effective signal extraction always requires prior knowledge or hypothesis, and hardly do without artificial judgment. We proposed bat algorithm sparse decomposition (BASD) to realize adaptive recognition and extraction of nonstationary signal in a noisy background. We designed two general atomics for typical signals, and developed dictionary training method based on correlation detection and Hilbert transform. The sparse decomposition was turned into an optimizing problem by introducing bat algorithm with optimized fitness function. By contrast with variational modal decomposition, it was indicated that BASD can effectively extract short time target without inducing global aliasing of local feature, and no preset mode number and late screening were needed.