The result of the emotional state induced by music may provide theoretical support and help for assisted music therapy. The key to assessing the state of emotion is feature extraction of the emotional electroencephalogram (EEG). In this paper, we study the performance optimization of the feature extraction algorithm. A public multimodal database for emotion analysis using physiological signals (DEAP) proposed by Koelstra et al. was applied. Eight kinds of positive and negative emotions were extracted from the dataset, representing the data of fourteen channels from the different regions of brain. Based on wavelet transform, δ, θ, α and β rhythms were extracted. This paper analyzed and compared the performances of three kinds of EEG features for emotion classification, namely wavelet features (wavelet coefficients energy and wavelet entropy), approximate entropy and Hurst exponent. On this basis, an EEG feature fusion algorithm based on principal component analysis (PCA) was proposed. The principal component with a cumulative contribution rate more than 85% was retained, and the parameters which greatly varied in characteristic root were selected. The support vector machine was used to assess the state of emotion. The results showed that the average accuracy rates of emotional classification with wavelet features, approximate entropy and Hurst exponent were respectively 73.15%, 50.00% and 45.54%. By combining these three methods, the features fused with PCA possessed an accuracy of about 85%. The obtained classification accuracy by using the proposed fusion algorithm based on PCA was improved at least 12% than that by using single feature, providing assistance for emotional EEG feature extraction and music therapy.
The diaphragm is the main respiratory muscle in the body. The onset detection of the surface diaphragmatic electromyography (sEMGdi) can be used in the respiratory rehabilitation training of the hemiparetic stroke patients, but the existence of electrocardiography (ECG) increases the difficulty of onset detection. Therefore, a method based on sample entropy (SampEn) and individualized threshold, referred to as SampEn method, was proposed to detect onset of muscle activity in this paper, which involved the extraction of SampEn features, the optimization of the SampEn parameters w and r0, the selection of individualized threshold and the establishment of the judgment conditions. In this paper, three methods were used to compare onset detection accuracy with the SampEn method, which contained root mean square (RMS) with wavelet transform (WT), Teager-Kaiser energy operator (TKE) with wavelet transform and TKE without wavelet transform, respectively. sEMGdi signals of 12 healthy subjects in 2 different breathing ways were collected for signal synthesis and methods detection. The cumulative sum of the absolute value of error τ was used as an judgement value to evaluate the accuracy of the four methods. The results show that SampEn method can achieve higher and more stable detection precision than the other three methods, which is an onset detection method that can adapt to individual differences and achieve high detection accuracy without ECG denoising, providing a basis for sEMGdi based respiratory rehabilitation training and real time interaction.
It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experimental results showed that the features selected in this paper were different between seizure and non-seizure. Among the eight patients, the average accuracy of three-level decomposition classification was 91.98%, the sensitivity was 90.15%, and the specificity was 93.81%. The work of this paper shows that our algorithm has excellent performance in the two classification of EEG signals of epileptic patients, and can detect the seizure period automatically and efficiently.
Motor imagery is often used in the fields of sports training and neurorehabilitation for its advantages of being highly targeted, easy to learn, and requiring no special equipment, and has become a major research paradigm in cognitive neuroscience. Transcranial direct current stimulation (tDCS), an emerging neuromodulation technique, modulates cortical excitability, which in turn affects functions such as locomotion. However, it is unclear whether tDCS has a positive effect on motor imagery task states. In this paper, 16 young healthy subjects were included, and the electroencephalogram (EEG) signals and near-infrared spectrum (NIRS) signals of the subjects were collected when they were performing motor imagery tasks before and after receiving tDCS, and the changes in multiscale sample entropy (MSE) and haemoglobin concentration were calculated and analyzed during the different tasks. The results found that MSE of task-related brain regions increased, oxygenated haemoglobin concentration increased, and total haemoglobin concentration rose after tDCS stimulation, indicating that tDCS increased the activation of task-related brain regions and had a positive effect on motor imagery. This study may provide some reference value for the clinical study of tDCS combined with motor imagery.
In recent years, exploring the physiological and pathological mechanisms of brain functional integration from the neural network level has become one of the focuses of neuroscience research. Due to the non-stationary and nonlinear characteristics of neural signals, its linear characteristics are not sufficient to fully explain the potential neurophysiological activity mechanism in the implementation of complex brain functions. In order to overcome the limitation that the linear algorithm cannot effectively analyze the nonlinear characteristics of signals, researchers proposed the transfer entropy (TE) algorithm. In recent years, with the introduction of the concept of brain functional network, TE has been continuously optimized as a powerful tool for nonlinear time series multivariate analysis. This paper first introduces the principle of TE algorithm and the research progress of related improved algorithms, discusses and compares their respective characteristics, and then summarizes the application of TE algorithm in the field of electrophysiological signal analysis. Finally, combined with the research progress in recent years, the existing problems of TE are discussed, and the future development direction is prospected.
As an emerging non-invasive brain stimulation technique, transcranial direct current stimulation (tDCS) has received increasing attention in the field of stroke disease rehabilitation. However, its efficacy needs to be further studied. The tDCS has three stimulation modes: bipolar-stimulation mode, anode-stimulation mode and cathode-stimulation mode. Nineteen stroke patients were included in this research (10 with left-hemisphere lesion and 9 with right). Resting electroencephalogram (EEG) signals were collected from subjects before and after bipolar-stimulation, anodal-stimulation, cathodal-stimulation, and pseudo-stimulation, with pseudo-stimulation serving as the control group. The changes of multi-scale intrinsic fuzzy entropy (MIFE) of EEG signals before and after stimulation were compared. The results revealed that MIFE was significantly greater in the frontal and central regions after bipolar-stimulation (P < 0.05), in the left central region after anodal-stimulation (P < 0.05), and in the frontal and right central regions after cathodal-stimulation (P < 0.05) in patients with left-hemisphere lesions. MIFE was significantly greater in the frontal, central and parieto-occipital joint regions after bipolar-stimulation (P < 0.05), in the left frontal and right central regions after anodal- stimulation (P < 0.05), and in the central and right occipital regions after cathodal-stimulation (P < 0.05) in patients with right-hemisphere lesions. However, the difference before and after pseudo-stimulation was not statistically significant (P > 0.05). The results of this paper showed that the bipolar stimulation pattern affected the largest range of brain areas, and it might provide a reference for the clinical study of rehabilitation after stroke.
Traditional sample entropy fails to quantify inherent long-range dependencies among real data. Multiscale sample entropy (MSE) can detect intrinsic correlations in data, but it is usually used in univariate data. To generalize this method for multichannel data, we introduced multivariate multiscale entropy into multiscale signals as a reflection of the nonlinear dynamic correlation. But traditional multivariate multiscale entropy has a large quantity of computation and costs a large period of time and space for more channel system, so that it can not reflect the correlation between variables timely and accurately. In this paper, therefore, an improved multivariate multiscale entropy embeds on all variables at the same time, instead of embedding on a single variable as in the traditional methods, to solve the memory overflow while the number of channels rise, and it is more suitable for the actual multivariate signal analysis. The method was tested in simulation data and Bonn epilepsy dataset. The simulation results showed that the proposed method had a good performance to distinguish correlation data. Bonn epilepsy dataset experiment also showed that the method had a better classification accuracy among the five data set, especially with an accuracy of 100% for data collection of Z and S.
To explore the relationship between the drug-seeking behavior, motivation of conditioned place preference (CPP) rats and the frontal association cortex (FrA) electroencephalogram (EEG) sample entropy, we in this paper present our studies on the FrA EEG sample entropy of control group rats and CPP group rats, respectively. We invested different behavior in four situations of the rat activities, i.e. rats were staying in black chamber of videoed boxes, those staying in white chamber of videoed boxes, those shuttling between black-white chambers and those shuttling between white-black chambers. The experimental results showed that, compared with the control group rats, the FrA EEG sample entropy of CPP rats staying in black chamber of video box and shuttling between white-black chambers had no significant difference. However, sample entropy is significantly smaller (P < 0.01) when heroin-induced group rats stayed in white chamber of video box and shuttled between black-white chambers. Consequently, the drug-seeking behavior and motivation of CPP rats correlated closely with the EEG sample entropy changes.
The analysis parameters for the characterization of heart rate variability (HRV) within a very short time (＜1 min) usually exhibit complicate variation patterns over time, which may easily interfere the judgment to the status of the cardiovascular system. In this study, long-term HRV sequence of 41 cases of healthy people (control group) and 25 cases of congestive heart failure (CHF) patients (experimental group) was divided into multiple segments of very short time series. The variation coefficient of the same HRV parameter under multiple segments of very short time series and the testing proportion with statistically significant differences under multiple interclass t-test were calculated. On this account, part of HRV analysis parameters under very short time were discussed to reveal the stability of difference of the cardiovascular system function under different status. Furthermore, with analyzing the receiver operating characteristic (ROC) curve and modeling the artificial neural network (ANN), the classification effects of these parameters between the control group and the experimental group were assessed. The results demonstrated that ① the indices of entropy of degree distribution based on the complex network analysis had a lowest variation coefficient and was sensitive to the pathological status (in 79.75% cases, there has statistically significant differences between the control group and experimental group), which can be served as an auxiliary index for clinical doctor to diagnose for CHF patient; ② after conducting ellipse fitting to Poincare plot, in 98.5% cases, there had statistically significant differences for the ratio of ellipse short-long axis (SDratio) between the control group and the experimental group; when modeling the ANN and solely adopting SDratio, the classification accuracy to the control group and experimental group was 71.87%, which demonstrated that SDratio might be acted as the intelligent diagnosis index for CHF patients; ③ however, more sensitive and robust indices were still needed to find out for the very-short HRV analysis and for the diagnosis of CHF patients as well.
Atrial fibrillation (AF) is one of the most common arrhythmias, which does great harm to patients. Effective methods were urgently required to prevent the recurrence of AF. Four methods were used to analyze RR sequence in this paper, and differences between Pre-AF (preceding an episode of AF) and Normal period (far away from episodes of AF) were analyzed to find discriminative criterion. These methods are: power spectral analysis, approximate entropy (ApEn) and sample entropy (SpEn) analysis, recurrence analysis and time series symbolization. The RR sequence data used in this research were downloaded from the Paroxysmal Atrial Fibrillation Prediction Database. Supporting vector machine (SVM) classification was used to evaluate the methods by calculating sensitivity, specificity and accuracy rate. The results showed that the comprehensive utilization of recurrence analysis parameters reached the highest accuracy rate (95%); power spectrum analysis took second place (90%); while the results of entropy analyses and time sequence symbolization were not satisfactory, whose accuracy were both only 70%. In conclusion, the recurrence analysis and power spectrum could be adopted to evaluate the atrial chaotic state effectively, thus having certain reference value for prediction of AF recurrence.