Synchronization analysis of electroencephalogram (EEG) and electromyogram (EMG) could reveal the functional corticomuscular coupling (FCMC) during the motor task in human. A novel method combining Gabor wavelet and transfer entropy (Gabor-TE) is proposed to quantitatively analyze the nonlinearly synchronous corticomuscular function coupling and direction characteristics under different steady-state force. Firstly, the Gabor wavelet transform method was used to acquire the local frequency-band signals of the EEG and EMG signals recorded from nine healthy controls simultaneously during performing grip task with four different steady-state forces. Secondly, the TE of local frequency-band was calculated and the unit area index of the transfer (ATE) was defined to quantitatively analyze the synchronous corticomuscular function coupling and direction characteristics under steady-state force. Lastly, the effect of EEG and EMG signal power spectrum on Gabor-TE analysis was explored. The results showed that the coupling strength in the beta band was stronger in EEG→EMG direction than in EMG→EEG direction, and the ATE values in the beta band in EEG→EMG direction decreased with the force increasing. It is also shown that the difference in TE values of gamma band present a varying regularity as the increase of force in both directions. In addition, EMG power spectrum was significantly correlated with the result of Gabor-TE inspecific frequency band. The results of our study confirmed that Gabor-TE can quantitatively describe the nonlinearly synchronous corticomuscular function coupling in both local frequency band and information transmission. The analysis of FCMC provides basic information for exploring the motor control and the evaluation of clinical rehabilitation.
Changes in the intrinsic characteristics of brain neural activities can reflect the normality of brain functions. Therefore, reliable and effective signal feature analysis methods play an important role in brain dysfunction and relative diseases early stage diagnosis. Recently, studies have shown that neural signals have nonlinear and multi-scale characteristics. Based on this, researchers have developed the multi-scale entropy (MSE) algorithm, which is considered more effective when analyzing multi-scale nonlinear signals, and is generally used in neuroinformatics. The principles and characteristics of MSE and several improved algorithms base on disadvantages of MSE were introduced in the article. Then, the applications of the MSE algorithm in disease diagnosis, brain function analysis and brain-computer interface were introduced. Finally, the challenges of these algorithms in neural signal analysis will face to and the possible further investigation interests were discussed.
The motor nervous system transmits motion control information through nervous oscillations, which causes the synchronous oscillatory activity of the corresponding muscle to reflect the motion response information and give the cerebral cortex feedback, so that it can sense the state of the limbs. This synchronous oscillatory activity can reflect connectivity information of electroencephalography-electromyography (EEG-EMG) functional coupling. The strength of the coupling is determined by various factors including the strength of muscle contraction, attention, motion intention etc. It is very significant to study motor functional evaluation and control methods to analyze the changes of EEG-EMG synchronous coupling caused by different factors. This article mainly introduces and compares coherence and Granger causality of linear methods, the mutual information and transfer entropy of nonlinear methods in EEG-EMG synchronous coupling, and summarizes the application of each method, so that researchers in related fields can understand the current research progress on analysis methods of EEG-EMG synchronous systematically.
In order to eliminate the influence of motion artifacts, high-frequency noise and baseline drift on photoplethysmographic (PPG), and to obtain the accurate value of heart rate in motion state, this paper proposed a de-noising method of PPG signal based on normalized least mean square (NLMS) adaptive filtering combining ensemble empirical mode decomposition(EEMD). Firstly, the PPG signal containing noise is passed through an adaptive filter with a 3-axis acceleration sensor as a reference signal to filter out motion artifacts. Secondly, the PPG signal is decomposed by EEMD to obtain a series of intrinsic modal function (IMF) according to the frequency from high to low. The threshold range of the signal is judged by the permutation entropy (PE) criterion, thereby filtering out the high frequency noise and the baseline drift. The experimental results show that the Pearson correlation coefficient between the calculated heart rate of PPG signal and the standard heart rate based on electrocardiogram (ECG) signal is 0.731 and the average absolute error percentage is 6.10% under different motion states, which indicates that the method can accurately calculate the heart rate in moving state and is beneficial to the physiological monitoring under the state of human motion.
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.
The automatic classification of epileptic electroencephalogram (EEG) is significant in the diagnosis and therapy of epilepsy. A classification algorithm for epileptic EEG based on wavelet multiscale analysis and extreme learning machine (ELM) is proposed in this paper. Firstly, wavelet multiscale analysis is applied to the original EEG to extract its sub-bands. Then, two nonlinear methods, i.e. Hurst exponent (Hurst) and sample entropy (SamEn) are used to the feature extraction of EEG and its sub-bands. Finally, ELM algorithm is employed in epileptic EEG classification with the nonlinear features. The proposed method in this paper achieved 99.5% classification accuracy for the discrimination between epileptic ictal and interictal EEG. The result implies that this method has good prospects in the diagnosis and therapy of epilepsy.
Vigilance is defined as the ability to maintain attention for prolonged periods of time. In order to explore the variation of brain vigilance in work process, we designed addition and subtraction experiment with numbers of three digits to induce the vigilance to change, combined it with psychomotor vigilance task (PVT) to measure this process of electroencephalogram (EEG), extracted and analyzed permutation entropy (PE) of 11 cases of subjects' EEG and made a brief comparison with nonlinear parameter sample entropy (SE). The experimental results showed that:PE could well reflect the dynamic changes of EEG when vigilance decreases, and has advantages of fast arithmetic speed, high noise immunity, and low requirements for EEG length. This can be used as a measure of the brain vigilance indicators.
Wavelet entropy is a quantitative index to describe the complexity of signals. Continuous wavelet transform method was employed to analyze the spontaneous electroencephalogram (EEG) signals of mild, moderate and severe Alzheimer's disease (AD) patients and normal elderly control people in this study. Wavelet power spectrums of EEG signals were calculated based on wavelet coefficients. Wavelet entropies of mild, moderate and severe AD patients were compared with those of normal controls. The correlation analysis between wavelet entropy and MMSE score was carried out. There existed significant difference on wavelet entropy among mild, moderate, severe AD patients and normal controls (P<0.01). Group comparisons showed that wavelet entropy for mild, moderate, severe AD patients was significantly lower than that for normal controls, which was related to the narrow distribution of their wavelet power spectrums. The statistical difference was significant (P<0.05). Further studies showed that the wavelet entropy of EEG and the MMSE score were significantly correlated (r=0.601-0.799, P<0.01). Wavelet entropy is a quantitative indicator describing the complexity of EEG signals. Wavelet entropy is likely to be an electrophysiological index for AD diagnosis and severity assessment.
The integral and individual-scale wavelet entropy of electroencephalogram (EEG) were employed to investigate the information complexity in EEG and to explore the dynamic mechanism of child absence epilepsy (CAE). The digital EEG signals were collected from patients with CAE and normal controls. Time-frequency features were extracted by continuous wavelet transformation. Individual scale power spectrum characteristics were represented by wavelet-transform. The integral and individual-scale wavelet entropy of EEG were computed on the basis of individual scale power spectrum. The evolutions of wavelet entropy across ictal EEG of CAE were investigated and compared with normal controls. The integral wavelet entropy of ictal EEG is lower than inter-ictal EEG for CAE, and it also lower than normal controls. The individual-scale wavelet entropies of 12th scale (centered at 3 Hz) of ictal EEG in CAE was significantly higher than normal controls. The individual-scale wavelet entropies for α band (centered at 10 Hz) of ictal EEG in CAE were much lower than normal controls. The integral wavelet entropy of EEG can be considered as a quantitative parameter of complexity for EEG signals. The complexity of ictal EEG for CAE is obviously declined in CAE. The wavelet entropies declined could become quantitative electrophysiological parameters for epileptic seizures, and it also could provide a theoretical basis for the study of neuromodulation techniques in epileptic seizures.
The purpose of this study is to compare the differences among neck muscle fatigue evaluation algorithms and to find a more effective algorithm which can provide a human factor quantitative evaluation method for neck muscle fatigue during bending over the desk. We collected surface electromyography signal of sternocleidomastoid muscle of 15 subjects using wireless physiotherapy Bio-Radio when they bent over the desk using memory pillows for 12 minutes. Five algorithms including mean power frequency, spectral moments ratio, discrete wavelet transform, fuzzy approximation entropy and the complexity algorithms were used to calculate the corresponding muscle fatigue index. The least squares method was used to calculate the corresponding coefficient of determination R2 and slope k of the linear regression of the muscle fatigue metric. The coefficient of determination R2 evaluates anti-interference ability of algorithms. The maximum vertical distance Lmax which is obtained by the Kolmogorov-Smirnov test for the slopes k evaluates the ability to distinguish fatigue of algorithms. The results indicate that in the aspect of anti-interference ability, the fuzzy approximation entropy has the largest R2 when using memory pillows with different heights. When the fuzzy approximate entropy is compared with average power frequency or the discrete wavelet transform, the differences are significant (P < 0.05). In terms of distinguishing the degree of fatigue, the approximate entropy is still the largest, with a maximum of 0.496 7. Fuzzy approximation entropy is superior to other algorithms in ability of anti-interference and distinguishing fatigue. Therefore, fuzzy approximation entropy can be used as a better evaluation algorithm in the evaluation of cervical muscle fatigue.