Envelope analysis links oscillatory and arrhythmic EEG activities to two types of neuronal synchronization

An Innovative EEG-Based Pain Identification and Quantification: A Pilot Study

OBJECTIVE

The present pilot study aimed to propose an innovative scale-independent measure based on electroencephalographic (EEG) signals for the identification and quantification of the magnitude of chronic pain.

METHODS

EEG data were collected from three groups of participants at rest: seven healthy participants with pain, 15 healthy participants submitted to thermal pain, and 66 participants living with chronic pain. Every 30 s, the pain intensity score felt by the participant was also recorded. Electrodes positioned in the contralateral motor region were of interest. After EEG preprocessing, a complex analytical signal was obtained using Hilbert transform, and the upper envelope of the EEG signal was extracted. The average coefficient of variation of the upper envelope of the signal was then calculated for the beta (13-30 Hz) band and proposed as a new EEG-based indicator, namely Piqβ, to identify and quantify pain.

MAIN RESULTS

The main results are as follows: (1) A Piqβ threshold at 10%, that is, Piqβ ≥ 10%, indicates the presence of pain, and (2) the higher the Piqβ (%), the higher the extent of pain.

CONCLUSIONS

This finding indicates that Piqβ can objectively identify and quantify pain in a population living with chronic pain. This new EEG-based indicator can be used for objective pain assessment based on the neurophysiological body response to pain.

SIGNIFICANCE

Objective pain assessment is a valuable decision-making aid and an important contribution to pain management and monitoring.

Instability of non-REM sleep in older women evaluated by sleep-stage transition and envelope analyses

Study objective

Traditionally, age-related deterioration of sleep architecture in older individuals has been evaluated by visual scoring of polysomnographic (PSG) recordings with regard to total sleep time and latencies. In the present study, we additionally compared the non-REM sleep (NREM) stage and delta, theta, alpha, and sigma wave stability between young and older subjects to extract features that may explain age-related changes in sleep.

Methods

Polysomnographic recordings were performed in 11 healthy older (72.6 ± 2.4 years) and 9 healthy young (23.3 ± 1.1 years) females. In addition to total sleep time, the sleep stage, delta power amplitude, and delta, theta, alpha, and sigma wave stability were evaluated by sleep stage transition analysis and a novel computational method based on a coefficient of variation of the envelope (CVE) analysis, respectively.

Results

In older subjects, total sleep time and slow-wave sleep (SWS) time were shorter whereas wake after sleep onset was longer. The number of SWS episodes was similar between age groups, however, sleep stage transition analysis revealed that SWS was less stable in older individuals. NREM sleep stages in descending order of delta power were: SWS, N2, and N1, and delta power during NREM sleep in older subjects was lower than in young subjects. The CVE of the delta-band is an index of delta wave stability and showed significant differences between age groups. When separately analyzed for each NREM stage, different CVE clusters in NREM were clearly observed between young and older subjects. A lower delta CVE and amplitude were also observed in older subjects compared with young subjects in N2 and SWS. Additionally, lower CVE values in the theta, alpha and sigma bands were also characteristic of older participants.

Conclusion

The present study shows a decrease of SWS stability in older subjects together with a decrease in delta wave amplitude. Interestingly, the decrease in SWS stability coincided with an increase in short-term delta, theta, sigma, and alpha power stability revealed by lower CVE. Loss of electroencephalograms (EEG) variability might be a useful marker of brain age.

Towards the Objective Identification of the Presence of Pain Based on Electroencephalography Signals' Analysis: A Proof-of-Concept

This proof-of-concept study explores the potential of developing objective pain identification based on the analysis of electroencephalography (EEG) signals. Data were collected from participants living with chronic fibromyalgia pain (n = 4) and from healthy volunteers (n = 7) submitted to experimental pain by the application of capsaicin cream (1%) on the right upper trapezius. This data collection was conducted in two parts: (1) baseline measures including pain intensity and EEG signals, with the participant at rest; (2) active measures collected under the execution of a visuo-motor task, including EEG signals and the task performance index. The main measure for the objective identification of the presence of pain was the coefficient of variation of the upper envelope (CVUE) of the EEG signal from left fronto-central (FC5) and left temporal (T7) electrodes, in alpha (8-12 Hz), beta (12-30 Hz) and gamma (30-43 Hz) frequency bands. The task performance index was also calculated. CVUE (%) was compared between groups: those with chronic fibromyalgia pain, healthy volunteers with "No pain" and healthy volunteers with experimentally-induced pain. The identification of the presence of pain was determined by an increased CVUE in beta (CVUE_β) from the EEG signals captured at the left FC5 electrode. More specifically, CVUE_β increased up to 20% in the pain condition at rest. In addition, no correlation was found between CVUE_β and pain intensity or the task performance index. These results support the objective identification of the presence of pain based on the quantification of the coefficient of variation of the upper envelope of the EEG signal.

DEEMD-SPP: A Novel Framework for Emotion Recognition Based on EEG Signals

Electroencephalography (EEG) is one of the most widely-used biosignal capturing technology for investigating brain activities, cognitive diseases, and affective disorders. To understand the underlying principles of brain activities and affective disorders using EEG data, one of the fundamental tasks is to accurately identify emotions from EEG signals, which has attracted huge attention in the field of affective computing. To improve the accuracy and effectiveness of emotion recognition based on EEG data, previous studies have successfully developed numerous feature extraction methods and classifiers. Among them, ensemble empirical mode decomposition (EEMD) is an efficient signal decomposition technique for extracting EEG features. It can alleviate the mode-mixing problem by adding white noise to the source signal. However, there remain some issues when applying this method to recognition tasks. As the added noise cannot be filtered completely, spurious modes are generated due to the residual noise. Therefore, it is crucial to perform intrinsic mode function (IMF) selection to find the most valuable IMF components that represent brain activities. Furthermore, the number of decomposed IMFs is various to different original signals, thus how to unify feature dimensions needs better solutions. To solve these issues, we propose a novel forecasting framework, named DEEMD-SPP, to identify emotions from EEG signals, based on the combination of denoising ensemble empirical mode decomposition (DEEMD) and Spatial Pyramid Pooling Network (SPP-Net). First, DEEMD is proposed to decompose the EEG signals, which effectively eliminates residual noise in the IMFs and selects the most valuable IMFs. Second, time-domain and frequency-domain features are extracted from the selected IMFs. Finally, SPP-net is employed as the classifier to recognize emotions, which can effectively transform various-sized feature maps into fixed-sized feature vectors through the pyramid pooling layer. The experimental results demonstrate that our proposed DEEMD-SPP framework can effectively reduce the effect of spike-in white noise, accurately extract EEG features, and significantly improve the performance of emotion recognition.

Shift-invariant waveform learning on epileptic ECoG

Seizure detection algorithms must discriminate abnormal neuronal activity associated with a seizure from normal neural activity in a variety of conditions. Our approach is to seek spatiotemporal waveforms with distinct morphology in electrocorticographic (ECoG) recordings of epileptic patients that are indicative of a subsequent seizure (preictal) versus non-seizure segments (interictal). To find these waveforms we apply a shift-invariant k-means algorithm to segments of spatially filtered signals to learn codebooks of prototypical waveforms. The frequency of the cluster labels from the codebooks is then used to train a binary classifier that predicts the class (preictal or interictal) of a test ECoG segment. We use the Matthews correlation coefficient to evaluate the performance of the classifier and the quality of the codebooks. We found that our method finds recurrent non-sinusoidal waveforms that could be used to build interpretable features for seizure prediction and that are also physiologically meaningful.

Exercise improves the quality of slow-wave sleep by increasing slow-wave stability

Exercise can improve sleep by reducing sleep latency and increasing slow-wave sleep (SWS). Some studies, however, report adverse effects of exercise on sleep architecture, possibly due to a wide variety of experimental conditions used. We examined the effect of exercise on quality of sleep using standardized exercise parameters and novel analytical methods. In a cross-over intervention study we examined the effect of 60 min of vigorous exercise at 60% [Formula: see text]max on the metabolic state, assessed by core body temperature and indirect calorimetry, and on sleep quality during subsequent sleep, assessed by self-reported quality of sleep and polysomnography. In a novel approach, envelope analysis was performed to assess SWS stability. Exercise increased energy expenditure throughout the following sleep phase. The subjective assessment of sleep quality was not improved by exercise. Polysomnography revealed a shorter rapid eye movement latency and reduced time spent in SWS. Detailed analysis of the sleep electro-encephalogram showed significantly increased delta power in SWS (N3) together with increased SWS stability in early sleep phases, based on delta wave envelope analysis. Although vigorous exercise does not lead to a subjective improvement in sleep quality, sleep function is improved on the basis of its effect on objective EEG parameters.

Sleep preserves subjective and sympathetic emotional response of memories

Sleep consolidates episodic content of emotional memories. Whether it likewise preserves or, to the contrary, depotentiates the emotional response associated with memory content is unclear, as there is conflicting evidence. In the current study, we investigated the influence of an afternoon nap (2-hr nap opportunity) on emotional responses of memories using multiple simultaneous measures. Young adults viewed 45 negative and 45 neutral pictures before taking a nap (measured with polysomnography) or remaining awake. Following the nap or wake period, participants viewed the same pictures intermixed with novel ones and indicated whether they remembered each picture. Emotional response to each picture was measured at both time points both subjectively, with valence and arousal ratings, and objectively, with recordings of electrodermal activity, electrocardiography, and corrugator supercilii electromyography. Compared to waking, a nap led to preserved subjective valence for negative pictures and preserved/increased skin conductance response in general. On the other hand, heart rate deceleration response decreased over the nap compared to wake interval, and this result was not influenced by picture type. These data suggest that sleep consolidates aspects of both subjective and physiological emotional response associated with episodic memory content. While sympathetic response appears to be preserved over sleep, parasympathetic response may be diminished.

Altered intra- and inter-regional functional connectivity of the visual cortex in individuals with peripheral vision loss due to retinitis pigmentosa

This study investigated changes in intra- and inter-regional functional connectivity (FC) in individuals with retinitis pigmentosa (RP) by using regional homogeneity (ReHo) and FC methods. Sixteen RP individuals and 14 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging scans (fMRI). A combined ReHo and FC method was conducted to evaluate synchronization of brain activity. Compared with HCs, RP individuals had significantly lower ReHo values in the bilateral lingual gyrus/cerebellum posterior lobe (LGG/CPL). In FC analysis, the RP group showed decreased positive FC relative to the HC group, from bilateral LGG/CPL to bilateral LGG/cuneus (CUN) and to left postcentral gyrus (PosCG). In contrast, the RP group showed increased negative FC relative to the HC group, from bilateral LGG/CPL to bilateral thalamus, and decreased negative FC from bilateral LGG/CPL to right middle frontal gyrus (MFG), and to left inferior parietal lobule (IPL). Moreover, ReHo values of the bilateral LGG/CPL showed negative correlations with the duration of RP. FC values of the bilateral LGG/CPL-left IPL showed negative correlations with best-corrected visual acuity (BCVA) of the right eye and left eye in RP individuals. Our results reveal reduced synchronicity of neural activity changes in the primary visual area in RP individuals. Moreover, RP individuals showed intrinsic visual network disconnection and reorganization of the retino-thalamocortical pathway and dorsal visual stream, suggesting impaired visuospatial and stereoscopic vision.

Emotional Memory Moderates the Relationship Between Sigma Activity and Sleep-Related Improvement in Affect

Sleep is essential for regulating mood and affect, and it also consolidates emotional memories. The mechanisms underlying these effects may overlap. Here, we investigated whether the influence of sleep on affect may be moderated by emotional memory consolidation. Young adults viewed 45 negative and 45 neutral pictures before taking an afternoon nap measured with polysomnography. Following the nap period, participants viewed the same pictures intermixed with novel ones and indicated whether they remembered each picture. Affect was measured with the Positive and Negative Affect Schedule (PANAS) at baseline before the initial picture viewing task, immediately following the initial picture viewing task, and following the nap. The ratio of positive to negative affect declined over the task period and recovered over the nap period. When controlling for pre-nap affect, NREM sigma activity significantly predicted post-nap affect. Memory for negative pictures moderated this relationship such that a positive association between sigma activity and affect occurred when memory was low but not when memory was high. These results indicate that emotional memory consolidation influences the relationship between nap physiology and mood.