Multi-Scale Permutation Entropy: A Potential Measure for the Impact of Sleep Medication on Brain Dynamics of Patients with Insomnia

Insomnia is a common sleep disorder that is closely associated with the occurrence and deterioration of cardiovascular disease, depression and other diseases. The evaluation of pharmacological treatments for insomnia brings significant clinical implications. In this study, a total of 20 patients with mild insomnia and 75 healthy subjects as controls (HC) were included to explore alterations of electroencephalogram (EEG) complexity associated with insomnia and its pharmacological treatment by using multi-scale permutation entropy (MPE). All participants were recorded for two nights of polysomnography (PSG). The patients with mild insomnia received a placebo on the first night (Placebo) and temazepam on the second night (Temazepam), while the HCs had no sleep-related medication intake for either night. EEG recordings from each night were extracted and analyzed using MPE. The results showed that MPE decreased significantly from pre-lights-off to the period during sleep transition and then to the period after sleep onset, and also during the deepening of sleep stage in the HC group. Furthermore, results from the insomnia subjects showed that MPE values were significantly lower for the Temazepam night compared to MPE values for the Placebo night. Moreover, MPE values for the Temazepam night showed no correlation with age or gender. Our results indicated that EEG complexity, measured by MPE, may be utilized as an alternative approach to measure the impact of sleep medication on brain dynamics.     

A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.     

Approximate Entropy of Brain Network in the Study of Hemispheric Differences

Human brain, a dynamic complex system, can be studied with different approaches, including linear and nonlinear ones. One of the nonlinear approaches widely used in electroencephalographic (EEG) analyses is the entropy, the measurement of disorder in a system. The present study investigates brain networks applying approximate entropy (ApEn) measure for assessing the hemispheric EEG differences; reproducibility and stability of ApEn data across separate recording sessions were evaluated. Twenty healthy adult volunteers were submitted to eyes-closed resting EEG recordings, for 80 recordings. Significant differences in the occipital region, with higher values of entropy in the left hemisphere than in the right one, show that the hemispheres become active with different intensities according to the performed function. Besides, the present methodology proved to be reproducible and stable, when carried out on relatively brief EEG epochs but also at a 1-week distance in a group of 36 subjects. Nonlinear approaches represent an interesting probe to study the dynamics of brain networks. ApEn technique might provide more insight into the pathophysiological processes underlying age-related brain disconnection as well as for monitoring the impact of pharmacological and rehabilitation treatments.     

Coupling of simultaneously acquired electrophysiological and haemodynamic responses during visual stimulation

We investigate the relationship between the temporal variation in the magnitude of occipital visual evoked potentials (VEPs) and of haemodynamic measures of brain activity obtained using both blood oxygenation level dependent (BOLD) and perfusion sensitive (ASL) functional magnetic resonance imaging (fMRI). Volunteers underwent a continuous BOLD fMRI scan and/or a continuous perfusion-sensitive (gradient and spin echo readout) ASL scan, during which 30 second blocks of contrast reversing visual stimuli (at 4 Hz) were interleaved with 30 second blocks of rest (visual fixation). Electroencephalography (EEG) and fMRI were simultaneously recorded and following EEG artefact cleaning, VEPs were averaged across the whole stimulation block (120 reversals, VEP₁₂₀) and at a finer timescale (15 reversals, VEP₁₅). Both BOLD and ASL time-series were linearly modelled to establish: (1) the mean response to visual stimulation, (2) transient responses at the start and end of each stimulation block, (3) the linear decrease between blocks, (4) the nonlinear between-block variation (covariation with VEP₁₂₀), (5) the linear decrease within block and (6) the nonlinear variation within block (covariation with VEP₁₅).     

Relativity Between Extrication Wave of Mental Load (EML) and Emot''''on

This experiment which was divided into three items studied the relativity between normal human emotion and extrication wave of mental load (EML) in a paradigm of combining CNV feedback with EML. Fourteen young persons were taken as subjects. After Fz, Cz, Pz, P_3—T_5 and P_4—T_6 were recorded, shorter EML latency and higher EML amplitude were caused by positive emotion in comparison with those caused by negative emotion. EML changes of the five recorded points were consistent under the effects of positive and negative emotions. This experiment also discussed the question of EML brain sources. Under the emotional conditions provided in the present experiment, EML possibly can be regarded as ERP index reflecting its positive and negative attributes.     

EEG quality during simultaneous functional MRI of interictal epileptiform discharges

This article concerns the evaluation of the quality of interictal epileptiform EEG discharges recorded throughout simultaneous echo planar imaging (EPI). BOLD (blood oxygen level dependent) functional MRI (fMRI) images were acquired continuously on a patient with intractable epilepsy. EEG was sampled simultaneously, during and after imaging, with removal of pulse and imaging artifacts by subtraction of channel-specific running averages. Contiguous EEG epochs recorded with and without fMRI (fMRI+ve vs. fMRI−ve) were next randomized and presented to two blinded observers. Epileptiform discharges were identified retrospectively, and comparison was made in terms of the number of identified events, their amplitude, and spatiotemporal distribution. A spectral analysis was also performed on the EEG. In the randomized comparison of EEG segments, 80 (fMRI+ve) vs. 69 (fMRI−ve) discharges were noted with good interobserver agreement (69%). There were no significant differences in amplitude or spatio-temporal distribution. Comparison of the events detected and measured by two expert observers demonstrated that the Interictal Epileptiform Discharge (IED) characteristics were indistinguishable with and without scanning. We review briefly the existing literature on EEG recording quality for combined EEG/fMRI.     

Spatio-Temporal Dynamics of Entropy in EEGS during Music Stimulation of Alzheimer’s Disease Patients with Different Degrees of Dementia

Music has become a common adjunctive treatment for Alzheimer’s disease (AD) in recent years. Because Alzheimer’s disease can be classified into different degrees of dementia according to its severity (mild, moderate, severe), this study is to investigate whether there are differences in brain response to music stimulation in AD patients with different degrees of dementia. Seventeen patients with mild-to-moderate dementia, sixteen patients with severe dementia, and sixteen healthy elderly participants were selected as experimental subjects. The nonlinear characteristics of electroencephalogram (EEG) signals were extracted from 64-channel EEG signals acquired before, during, and after music stimulation. The results showed the following. (1) At the temporal level, both at the whole brain area and sub-brain area levels, the EEG responses of the mild-to-moderate patients showed statistical differences from those of the severe patients (p < 0.05). The nonlinear characteristics during music stimulus, including permutation entropy (PmEn), sample entropy (SampEn), and Lempel–Ziv complexity (LZC), were significantly higher in both mild-to-moderate patients and healthy controls compared to pre-stimulation, while it was significantly lower in severe patients. (2) At the spatial level, the EEG responses of the mild-to-moderate patients and the severe patients showed statistical differences (p < 0.05), showing that as the degree of dementia progressed, fewer pairs of EEG characteristic showed significant differences among brain regions under music stimulation. In this paper, we found that AD patients with different degrees of dementia had different EEG responses to music stimulation. Our study provides a possible explanation for this discrepancy in terms of the pathological progression of AD and music cognitive hierarchy theory. Our study has adjunctive implications for clinical music therapy in AD., potentially allowing for more targeted treatment. Meanwhile, the variations in the brains of Alzheimer’s patients in response to music stimulation might be a model for investigating the neural mechanism of music perception.     

士兵可穿戴下肢外骨骼机器人多元感知方法研究

下肢外骨骼机器人是一种可穿戴且融合了多种机器人技术的复杂人-机系统。它将人类的智慧与机器人强壮的能力有效地结合起来,最大限度地提高人体的机动力和耐力,这为提升单兵作战系统的能力创造了条件。鉴于下肢外骨骼机器人在作战、后勤保障时可能遇到的复杂地形、多变随机的任务等,仅通过基于既定的典型步态规划程序驱动执行已知的特定动作,难以保证人机间的耦合性和动作的高随意性切换。为此,模拟并提炼出士兵常见的六种下肢动作作为后续研究,然后分析了下肢外骨骼机器人的感知控制原理,并提出了基于脑电预判感知、肌电精确感知和光纤实时校正的多信息融合的感知方法,强调将人的智能参与到机器人控制中,以期推进士兵可穿戴下肢外骨骼机器人的实用化。     

Regularized RKHS-Based Subspace Learning for Motor Imagery Classification

Brain–computer interface (BCI) technology allows people with disabilities to communicate with the physical environment. One of the most promising signals is the non-invasive electroencephalogram (EEG) signal. However, due to the non-stationary nature of EEGs, a subject’s signal may change over time, which poses a challenge for models that work across time. Recently, domain adaptive learning (DAL) has shown its superior performance in various classification tasks. In this paper, we propose a regularized reproducing kernel Hilbert space (RKHS) subspace learning algorithm with K-nearest neighbors (KNNs) as a classifier for the task of motion imagery signal classification. First, we reformulate the framework of RKHS subspace learning with a rigorous mathematical inference. Secondly, since the commonly used maximum mean difference (MMD) criterion measures the distribution variance based on the mean value only and ignores the local information of the distribution, a regularization term of source domain linear discriminant analysis (SLDA) is proposed for the first time, which reduces the variance of similar data and increases the variance of dissimilar data to optimize the distribution of source domain data. Finally, the RKHS subspace framework was constructed sparsely considering the sensitivity of the BCI data. We test the proposed algorithm in this paper, first on four standard datasets, and the experimental results show that the other baseline algorithms improve the average accuracy by 2–9% after adding SLDA. In the motion imagery classification experiments, the average accuracy of our algorithm is 3% higher than the other algorithms, demonstrating the adaptability and effectiveness of the proposed algorithm.     

Classification of spontaneous EEG signals in migraine

We set up a classification system able to detect patients affected by migraine without aura, through the analysis of their spontaneous EEG patterns. First, the signals are characterized by means of wavelet-based features, than a supervised neural network is used to classify the multichannel data. For the feature extraction, scale-dependent and scale-independent methods are considered with a variety of wavelet functions. Both the approaches provide very high and almost comparable classification performances. A complete separation of the two groups is obtained when the data are plotted in the plane spanned by two suitable neural outputs.