Alpha Band Research Articles

Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing

Epilepsy affects millions worldwide, making timely seizure detection crucial for effective treatment and enhanced well-being. Electroencephalogram (EEG) analysis offers a non-intrusive solution, but its visual interpretation is prone to errors and requires a lot of time. Many existing works focus solely on achieving competitive levels of accuracy without considering processing speed or the computational complexity of their models. This study aimed to develop an automated technique for identifying epileptic seizures in EEG data through analysis methods. The efforts have been primarily focused on achieving high accuracy results by operating exclusively within a narrow frequency band of the signal, while also aiming to minimize computational complexity. In this article, a new automated approach is presented for seizure detection by combining signal processing and machine learning techniques. The proposed method comprises four stages: (1) Preprocessing: Savitzky–Golay filter to remove the background noise. (2) Decomposition: discrete wavelet transform (DWT) to extract spontaneous alpha and beta frequency bands. (3) Feature extraction: six features (mean, standard deviation, skewness, kurtosis, energy, and entropy) are computed for each frequency band. (4) Classification: a support vector machine (SVM) method classifies signals as normal or containing a seizure. The method was assessed using two publicly available EEG datasets. For the alpha band, the highest achieved accuracy was 92.82%, and for the beta band it was 90.55%, which demonstrates adequate capability in both bands for accurate seizure detection. Furthermore, the obtained low computational cost suggests a potentially valuable application in real-time assessment scenarios. The obtained results indicate its capacity as a valuable instrument for diagnosing epilepsy and monitoring patients. Further research is necessary for clinical validation and potential real-time deployment.

Power spectral density-based resting-state EEG classification of first-episode psychosis

Historically, the analysis of stimulus-dependent time–frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with first-episode psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian process classifier (GPC), to demonstrate the practicality of resting-state power spectral density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders.

Conflicting Sensory Information Sharpens the Neural Representations of Early Selective Visuospatial Attention.

Adaptive behaviors require the ability to resolve conflicting information caused by the processing of incompatible sensory inputs. Prominent theories of attention have posited that early selective attention helps mitigate cognitive interference caused by conflicting sensory information by facilitating the processing of task-relevant sensory inputs and filtering out behaviorally irrelevant information. Surprisingly, many recent studies that investigated the role of early selective attention on conflict mitigation have failed to provide positive evidence. Here, we examined changes in the selectivity of early visuospatial attention in male and female human subjects performing an attention-cueing Eriksen flanker task, where they discriminated the shape of a visual target surrounded by congruent or incongruent distractors. We used the inverted encoding model to reconstruct spatial representations of visual selective attention from the topographical patterns of amplitude modulations in alpha band oscillations in scalp EEG (∼8-12 Hz). We found that the fidelity of the alpha-based spatial reconstruction was significantly higher in the incongruent compared with the congruent condition. Importantly, these conflict-related modulations in the reconstruction fidelity occurred at a much earlier time window than those of the lateralized posterior event-related potentials associated with target selection and distractor suppression processes, as well as conflict-related modulations in the frontocentral negative-going wave and midline-frontal theta oscillations (∼3-7 Hz), thought to track executive control functions. Taken together, our data suggest that conflict resolution is supported by the cascade of neural processes underlying early selective visuospatial attention and frontal executive functions that unfold over time.

A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network.

Unlocking task-related EEG spectra is crucial for neuroscience. Traditional convolutional neural networks (CNNs) effectively extract these features but face limitations like overfitting due to small datasets. To address this issue, we propose a lightweight CNN and assess its interpretability through the fully connected layer (FCL). Initially tested with two tasks (Task 1: open vs closed eyes, Task 2: interictal vs ictal stage), the CNN demonstrated enhanced spectral features in the alpha band for Task 1 and the theta band for Task 2, aligning with established neurophysiological characteristics. Subsequent experiments on two brain-computer interface tasks revealed a correlation between delta activity (around 1.55 Hz) and hand movement, with consistent results across pericentral electroencephalogram (EEG) channels. Compared to recent research, our method stands out by delivering task-related spectral features through FCL, resulting in significantly fewer trainable parameters while maintaining comparable interpretability. This indicates its potential suitability for a wider array of EEG decoding scenarios.

Attention and Interoception Alter Perceptual and Neural Pain Signatures-A Case Study.

Fluctuations of chronic pain levels are determined by a complex interplay of cognitive, emotional and perceptual variables. We introduce a pain tracking platform composed of wearable neurotechnology and a smartphone application to measure and predict chronic pain levels and its interplay with other dimensions of experience. Our method measures, dynamically and at home, pain strength, phenomenal and neural time series collected with an online tool and low-density EEG. Here we used data from a single participant who performed an attention task at home for a period of 20 days to investigate the role of attention to different bodily systems in chronic pain. We show a relationship between emotions and pain strength while allocating attention to the heartbeat, the breathing, the affected or the unaffected limb. We found that pain was maximal when attending to the affected limb and decreased when the participant focused on his breathing or his heartbeat. These results provide interesting insights regarding the role of attention to interoceptive signals in chronic pain. We found power changes in the delta, theta, alpha and beta (but not in the gamma) band between the four attention conditions. However, there was no reliable association of these changes to pain intensity ratings. Theta power was higher when attention was directed to the unaffected limb compared to the others. Further, the pain ratings, when attending to unaffected limb, were associated with alpha and theta power band changes. Overall, we demonstrate that our neurophysiology and experience tracking platform can capture how body attention allocation alters the dynamics of subjective measures and its neural correlates. This research approach is proof of concept for the development of personalized clinical assessment tools and a testbed for behavioural, subjective and biomarkers characterization.

Modulation effects of imagery acupuncture and no-touch double-blinded placebo acupuncture, a cross-over pilot study

Both imagery and acupuncture are the oldest medical practices. Recently, we have developed a new treatment modality, video-guided acupuncture imagery treatment (VGAIT), which combines acupuncture and imagery. In this crossover study, we investigated the modulation effects of video-guided acupuncture imagery treatment compared with placebo acupuncture using no-touch double-blind placebo acupuncture needles and a no-treatment resting control. Pressure pain threshold and electroencephalogram (EEG) data were collected before and after each intervention. 12 healthy participants completed the study. Results showed that pressure pain thresholds were significantly increased after VGAIT compared to the resting control condition. In addition, we found that VGAIT, but not the no-touch placebo acupuncture or the resting control, significantly increased alpha and beta band power. Our findings demonstrate the potential of VGAIT as a remote therapeutic method (e-health treatment option) for pain and the value of no-touch double-blind placebo acupuncture in acupuncture research.

Possibilities of Skin Autofluorescence Reflecting the Characteristics of Diabetes Mellitus in Childhood

Objective: To measure skin autofluorescence in children and adolescents suffering from type 1 diabetes mellitus and evaluate its relationship with gender, age, experience and chronic complications of the disease. Materials and Methods: The study group included 47 children and adolescents with type 1 diabetes. Autofluorescence of the skin from the inner surface of the shoulder and nail of patients was measured using an original compact spectrofluorimeter based on the STS-VIS OCEAN OPTICS © USA microspectrometer with UVA excitation. Statistical analysis was carried out using StatsoftStatistica 12.0 software. The data is presented as a two-dimensional array. The UV LED signal was averaged and smoothed using the moving average method with a 10 nm window. Then the spectra were renormalized taking into account the found coefficients. The result of applying additional normalization is a decrease in the standard deviation. Results and Discussion: Significant differences were revealed in the skin fluorescence spectra of children of different ages. between age groups (5-7) and (8-12) is most significant in the region of the alpha band of oxyhemoglobin (540 nm) (p <0.005). When using I-normalization, the NADH peak region (p < 0.02) is significant with increasing disease duration. When studying the influence of gender factors on the level of skin autofluorescence, the most significant differences are found in the area of only the isosbestic points of deoxy and oxyhemoglobin 442 nm (p<10-7) and 491 nm (p<10-8). Significant differences in skin autofluorescence at the reference length were also obtained waves in the autofluorescence spectrum of 500 nm correspond to p<10-14, depending on the presence of complications. Conclusion: In Russia, as well as throughout the world, there is an increase in the incidence of type 1 diabetes mellitus. For early diagnosis of changes in carbohydrate metabolism and complications of the disease, a simple, accessible, non-invasive research method is needed. Taking into account the results of our study, when creating non-invasive methods for monitoring the state of carbohydrate metabolism, it is necessary to take into account gender and age characteristics, experience and the presence of complications of type 1 diabetes mellitus.

Changes in Spatiotemporal Dynamics of Default Network Oscillations between 19 and 29 Years of Age.

The exploration of functional resting-state brain developmental parameters and measures can help to improve scientific, psychological, and medical applications. The present work focussed on both traditional approaches, such as topographical power analyses at the signal space level, and advanced approaches, such as the exploration of age-related dynamics of source space data. The results confirmed the expectation that the third life decade would show a kind of stability in oscillatory signal and source-space-related parameters. However, from a source dynamics perspective, different frequency ranges appear to develop quite differently, as reflected in age-related sequential network communication profiles. Among other discoveries, the left anterior cingulate source location could be shown to reduce bi-directional network communication in the lower alpha band, whereas it differentiated its uni- and bidirectional communication dynamics to sub-cortical and posterior brain locations. Higher alpha oscillations enhanced communication dynamics between the thalamus and particularly frontal areas. In conclusion, resting-state data appear to be, at least in part, functionally reorganized in the default mode network, while quantitative measures, such as topographical power and regional source activity, did not correlate with age in the third life decade. In line with other authors, we suggest the further development of a multi-perspective approach in biosignal analyses.

A Novel Hybrid Feature Extraction Methods for EEG Based Emotions Classification Using Ensemble Approaches in HIED Dataset

Introduction: Emotions play a significant role in human relationships in everyday challenges. Human-machine interactions and the complexities of its research include humanoid robots which rely significantly on emotional queries. The present study recommends a novel methodology for human emotion identification using electroencephalogram (EEG) signals. Machine learning (ML) techniques based on electroencephalogram have demonstrated impressive results in distinguishing emotions. Method: The present study investigates five ensemble learning-based machine learning (EML) and five conventional machine learning (CML) algorithms for achieving the recognition of human emotions from EEG signals. The main objective of the current research study is to implement the optimal approach for classifying emotions and subsequently will achieve it by using physiological characteristics to categorise the seven main emotional states (stress, happy, sadness, anger, fear, disgust, and surprise). This research follows the study investigation of ensemble learning-based machine learning (EML) and conventional machine learning (CML) algorithms for achieving the recognition of human emotions from EEG signals. Initially, the EEG data in this study are categorized into theta, alpha, beta, and gamma bands using a DWT. Result: With this idea, the research elaborates on further decomposition based on bandseparated EEG signals known as intrinsic mode functions (IMFs) along with empirical mode decomposition is subsequently implemented. Then, 13 statistical characteristics are extracted from the IMFs using five multiclass EML algorithms: adaptive boosting, rotation random forest, bagging, random forest, and extreme gradient boost. The following procedure is carried out to construct an ML-based system. In the final phase, 10-fold cross-validation is used to assess the efficacy of these five EML algorithms. Accuracy, F1-score, and area-under-the-curve (AUC) are performance assessment metrics that are employed to compare the final results of these algorithms with the five CML techniques. Conclusion: The proposed method for EEG-based emotion analysis is significantly more effective and achieves greater outcomes than all of the techniques that were compared, based on the data from the experiments.

Adaptive and Compensatory Neural Signatures in Fibromyalgia: An Analysis of Resting-State and Stimulus-Evoked EEG Oscillations.

This study aimed to investigate clinical and physiological predictors of brain oscillatory activity in patients with fibromyalgia (FM), assessing resting-state power, event-related desynchronization (ERD), and event-related synchronization (ERS) during tasks. We performed a cross-sectional analysis, including clinical and neurophysiological data from 78 subjects with FM. Multivariate regression models were built to explore predictors of electroencephalography bands. Our findings show a negative correlation between beta oscillations and pain intensity; fibromyalgia duration is positively associated with increased oscillatory power at low frequencies and in the beta band; ERS oscillations in the theta and alpha bands seem to be correlated with better symptoms of FM; fatigue has a signature in the alpha band-a positive relationship in resting-state and a negative relationship in ERS oscillations. Specific neural signatures lead to potential clusters of neural adaptation, in which beta oscillatory activity in the resting state represents a more adaptive activity when pain levels are low and stimulus-evoked oscillations at lower frequencies are likely brain compensatory mechanisms. These neurophysiological changes may help to understand the impact of long-term chronic pain in the central nervous system and the descending inhibitory system in fibromyalgia subjects.

EEG-based driving intuition and collision anticipation using joint temporal-frequency multi-layer dynamic brain network.

Intuition plays a crucial role in human driving decision-making, and this rapid and unconscious cognitive process is essential for improving traffic safety. We used the first proposed multi-layer network analysis method, "Joint Temporal-Frequency Multi-layer Dynamic Brain Network" (JTF-MDBN), to study the EEG data from the initial and advanced phases of driving intuition training in the theta, alpha, and beta bands. Additionally, we conducted a comparative study between these two phases using multi-layer metrics as well as local and global metrics of single layers. The results show that brain region activity is more stable in the advanced phase of intuition training compared to the initial phase. Particularly in the alart state task, the JTF-MDBN demonstrated stronger connection strength. Multi-layer network analysis indicates that modularity is significantly higher for the non-alert state task than the alert state task in the alpha and beta bands. In the W4 time window (1 second before a collision), we identified significant features that can differentiate situations where a car collision is imminent from those where no collision occurs. Single-layer network analysis also revealed statistical differences in node strength and local efficiency for some EEG channels in the alpha and beta bands during the W4 and W5 time windows. Using these biomarkers to predict vehicle collision risk, the classification accuracy of a linear kernel SVM reached up to 87.5%, demonstrating the feasibility of predicting driving collisions through brain network biomarkers. These findings are important for the study of human intuition and the development of brain-computer interface-based intelligent driving hazard perception assistance systems.

Brain regulation training improves emotional competences in patients with alcohol use disorder.

Alcohol use disorder (AUD) is defined as the impaired ability to stop or control alcohol use despite adverse social, occupational, or health consequences and still represents one of the biggest challenges for society regarding health conditions, social consequences, and financial costs, including the high relapse rates after traditional alcohol rehabilitation treatment. Especially, the deficient emotional competence in AUD is said to play a key role in the development of AUD and hinders the interruption of substance compulsion, often leading to a viscous circle of relapse. Although the empirical evidence of a neurophysiological basis of AUD is solid and increases even further, clinical interventions based on neurophysiology are still rare for individuals with AUD. This randomized controlled trial investigates changes in emotional competences, alcohol-related cognitions, and drinking behavior before and after an established alcohol rehabilitation treatment (control group: nCG = 29) compared to before and after an optimized, add-on neurofeedback (NF) training (experimental group: nEG = 27). Improvements on the clinical-psychological level, i.e. increases in emotional competences as well as life satisfaction, were found after the experimental electroencephalography (EEG) NF training. Neurophysiological measurements via resting-state EEG indicate decreases in low beta frequency band, while alpha and theta bands remained unaffected.

Cross-frequency modulation of postural fluctuations and scalp EEG in older adults: error amplification feedback for rapid balance adjustments.

Virtual error amplification (VEA) in visual feedback enhances attentive control over postural stability, although the neural mechanisms are still debated. This study investigated the distinct cortical control of unsteady stance in older adults using VEA through cross-frequency modulation of postural fluctuations and scalp EEG. Thirty-seven community-dwelling older adults (68.1 ± 3.6years) maintained an upright stance on a stabilometer while receiving either VEA or real error feedback. Along with postural fluctuation dynamics, phase-amplitude coupling (PAC) and amplitude-amplitude coupling (AAC) were analyzed for postural fluctuations under 2Hz and EEG sub-bands (theta, alpha, and beta). The results revealed a higher mean frequency of the postural fluctuation phase (p = .005) and a greater root mean square of the postural fluctuation amplitude (p = .003) with VEA compared to the control condition. VEA also reduced PAC between the postural fluctuation phase and beta-band EEG in the left frontal (p = .009), sensorimotor (p = .002), and occipital (p = .018) areas. Conversely, VEA increased the AAC of posture fluctuation amplitude and beta-band EEG in FP2 (p = .027). Neither theta nor alpha band PAC or AAC were affected by VEA. VEA optimizes postural strategies in older adults during stabilometer stance by enhancing visuospatial attentive control of postural responses and facilitating the transition of motor states against postural perturbations through a disinhibitory process. Incorporating VEA into virtual reality technology is advocated as a valuable strategy for optimizing therapeutic interventions in postural therapy, particularly to mitigate the risk of falls among older adults.

The human brain deals with violating general color or depth knowledge in different time courses

Utilizing the high temporal resolution of event-related potentials (ERPs), we compared the time course of processing incongruent color versus 3D-depth information. Participants were asked to judge whether the food color (color condition) or 3D structure (3D-depth condition) was congruent or incongruent with their previous knowledge and experience. The behavioral results showed that the reaction times in the congruent 3D-depth condition were slower than those in the congruent color condition. The reaction times in the incongruent 3D-depth condition were slower than those in the incongruent color condition. The ERP results showed that incongruent color stimuli induced a larger N270, larger P300, and smaller N400 components in the fronto-central region than the congruent color stimuli. Incongruent 3D-depth stimuli induced a smaller N1 in the occipital region, larger P300 and smaller N400 in the parietal-occipital region than congruent 3D-depth stimuli. The time–frequency analysis found that incongruent color stimuli induced a larger theta band (360–580 ms) activation in the fronto-central region than congruent color stimuli. Incongruent 3D-depth stimuli induced larger alpha and beta bands (240–350 ms) activation in the parietal region than congruent 3D-depth stimuli. Our results suggest that the human brain deals with violating general color or depth knowledge in different time courses. We speculate that the depth perception conflict was dominated by solving the problem with visual processing, whereas the color perception conflict was dominated by solving the problem with semantic violation.

Bimanual coordination and spinal cord neuromodulation: how neural substrates of bimanual movements are altered by transcutaneous spinal cord stimulation

Humans use their arms in complex ways that often demand two-handed coordination. Neurological conditions limit this impressive feature of the human motor system. Understanding how neuromodulatory techniques may alter neural mechanisms of bimanual coordination is a vital step towards designing efficient rehabilitation interventions. By non-invasively activating the spinal cord, transcutaneous spinal cord stimulation (tSCS) promotes recovery of motor function after spinal cord injury. A multitude of research studies have attempted to capture the underlying neural mechanisms of these effects using a variety of electrophysiological tools, but the influence of tSCS on cortical rhythms recorded via electroencephalography remains poorly understood, especially during bimanual actions. We recruited 12 neurologically intact participants to investigate the effect of cervical tSCS on sensorimotor cortical oscillations. We examined changes in the movement kinematics during the application of tSCS as well as the cortical activation level and interhemispheric connectivity during the execution of unimanual and bimanual arm reaching movements that represent activities of daily life. Behavioral assessment of the movements showed improvement of movement time and error during a bimanual common-goal movement when tSCS was delivered, but no difference was found in the performance of unimanual and bimanual dual-goal movements with the application of tSCS. In the alpha band, spectral power was modulated with tSCS in the direction of synchronization in the primary motor cortex during unimanual and bimanual dual-goal movements and in the somatosensory cortex during unimanual movements. In the beta band, tSCS significantly increased spectral power in the primary motor and somatosensory cortices during the performance of bimanual common-goal and unimanual movements. A significant increase in interhemispheric connectivity in the primary motor cortex in the alpha band was only observed during unimanual tasks in the presence of tSCS. Our observations provide, for the first time, information regarding the supra-spinal effects of tSCS as a neuromodulatory technique applied to the spinal cord during the execution of bi- and unimanual arm movements. They also corroborate the suppressive effect of tSCS at the cortical level reported in previous studies. These findings may guide the design of improved rehabilitation interventions using tSCS for the recovery of upper-limb function in the future.

Multi-origins of pathological theta oscillation from neuron to network inferred by a combined data and model study with cubature Kalman filter

The brain rhythm is strongly associated with the brain function. Alzheimer's disease (AD) is characterized reflected by the brain rhythm switching from the alpha band (9–12 Hz) to the theta band (4–8 Hz), accompanied with the loss of brain function. However, extracting the implicating intrinsic characteristic variations of the brain network by utilizing the Electroencephalogram (EEG) information is challenging. Kaman observer, serving as an effective Bayesian technique, can provide a visualization service for probing the intrinsic characteristics underlying the pathological theta oscillations. This work first establishes an excitation-inhibitory neural network model and explores the role of the fraction of the inhibitory neurons and inhibitory synapses in the pathological theta oscillation. The results indicate that the reduced inhibitory neuronal proportion and attenuated inhibitory synaptic weight are the main neural bases of the frequency reduction of neural oscillation. Then, we further explore the intrinsic spiking characteristic by considering spike frequency adaptation (SFA) to the inhibitory neurons. The results show that the SFA reduces the firing rate of neurons, which facilitates the theta rhythm. The enhancement of SFA current by increasing the time constant of its gating variable can further decrease the theta frequency from 7 Hz to 4 Hz. Furthermore, for this high-dimensional nonlinear excitation-inhibitory neural network model, cubature Kalman filter (CKF) is employed to estimate the above potential variations from the EEG data. The observation results show that the attenuated trends of the inhibitory neuronal proportion and the decreased inhibitory SFA current result in the descending brain rhythm. Finally, the theoretical simulation is deduced by utilizing the mean field theory for simplifying high-dimensional model to verify the simulation results. The theoretical variations of inhibitory parameters and adaptation gating variable are consistent with the simulation results. In summary, we investigate the multi-origin factors related to inhibitory neuronal intrinsic characteristics from forward model simulation and inverse EEG estimation process. And we further verify the simulation and data-driven results by theoretical derivation. This work enhances the understanding of the systematic function of inhibitory intrinsic characteristics on pathological theta oscillation and provides an effective method to decode the dynamics underlying neural activities.

Individual Variability in Brain Connectivity Patterns and Driving-Fatigue Dynamics.

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals' brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.

The interictal suppression hypothesis is the dominant differentiator of seizure onset zones in focal epilepsy.

Successful surgical treatment of drug-resistant epilepsy traditionally relies on the identification of seizure onset zones (SOZs). Connectome-based analyses of electrographic data from stereo electroencephalography (SEEG) may empower improved detection of SOZs. Specifically, connectome-based analyses based on the interictal suppression hypothesis posit that when the patient is not having a seizure, SOZs are inhibited by non-SOZs through high inward connectivity and low outward connectivity. However, it is not clear whether there are other motifs that can better identify potential SOZs. Thus, we sought to use unsupervised machine learning to identify network motifs that elucidate SOZs and investigate if there is another motif that outperforms the ISH. Resting-state SEEG data from 81 patients with drug-resistant epilepsy undergoing a pre-surgical evaluation at Vanderbilt University Medical Center were collected. Directed connectivity matrices were computed using the alpha band (8-13 Hz). Principal component analysis (PCA) was performed on each patient's connectivity matrix. Each patient's components were analysed qualitatively to identify common patterns across patients. A quantitative definition was then used to identify the component that most closely matched the observed pattern in each patient. A motif characteristic of the interictal suppression hypothesis (high-inward and low-outward connectivity) was present in all individuals and found to be the most robust motif for identification of SOZs in 64/81 (79%) patients. This principal component demonstrated significant differences in SOZs compared to non-SOZs. While other motifs for identifying SOZs were present in other patients, they differed for each patient, suggesting that seizure networks are patient specific, but the ISH is present in nearly all networks. We discovered that a potentially suppressive motif based on the interictal suppression hypothesis was present in all patients, and it was the most robust motif for SOZs in 79% of patients. Each patient had additional motifs that further characterized SOZs, but these motifs were not common across all patients. This work has the potential to augment clinical identification of SOZs to improve epilepsy treatment.

A short simulated nature experience as an effective way to promote restoration from work-related stress.

Spending time in nature, and even watching images or videos of nature, has positive effects on one's mental state. However, cognitively stressful work is often performed indoors, in offices that lack easy access to nature during breaks. In this study, we investigated whether watching a 5-min audiovisual video that describes a first-person perspective walk on a forest path could help to restore one's mental state after cognitive stress. Participants were asked to perform cognitive stressor tasks, after which they were shown either a nature walk video or a control video. Subjective restoration was measured using self-reports before and after the videos, while electrodermal activity (EDA) and electroencephalography (EEG) were measured during the video-watching session. The results showed that experiencing the nature walk video enhanced subjective restoration more than watching the control video. Arousal of the autonomic nervous system, measured using EDA, decreased more during the nature walk video than during the control video. Additionally, activity in the EEG's upper theta band (6-8 Hz) and lower alpha band (8-10 Hz) increased during the nature walk video, suggesting that it induced a relaxed state of mind. Interestingly, the participants' connection with nature moderated the effects of the nature video. The subjective and physiological measures both suggest that watching a short, simulated nature walk may be beneficial in relaxing the mind and restoring one's mental state after cognitive stress.

Neural Filtering of Physiological Tremor Oscillations to Spinal Motor Neurons Mediates Short-Term Acquisition of a Skill Learning Task.

The acquisition of a motor skill involves adaptations of spinal and supraspinal pathways to alpha motoneurons. In this study, we estimated the shared synaptic contributions of these pathways to understand the neural mechanisms underlying the short-term acquisition of a new force-matching task. High-density surface electromyography (HDsEMG) was acquired from the first dorsal interosseous (FDI; 7 males and 6 females) and tibialis anterior (TA; 7 males and 4 females) during 15 trials of an isometric force-matching task. For two selected trials (pre- and post-skill acquisition), we decomposed the HDsEMG into motor unit spike trains, tracked motor units between trials, and calculated the mean discharge rate and the coefficient of variation of interspike interval (COVISI). We also quantified the post/pre ratio of motor units' coherence within delta, alpha, and beta bands. Force-matching improvements were accompanied by increased mean discharge rate and decreased COVISI for both muscles. Moreover, the area under the curve within alpha band decreased by ∼22% (TA) and ∼13% (FDI), with no delta or beta bands changes. These reductions correlated significantly with increased coupling between force/neural drive and target oscillations. These results suggest that short-term force-matching skill acquisition is mediated by attenuation of physiological tremor oscillations in the shared synaptic inputs. Supported by simulations, a plausible mechanism for alpha band reductions may involve spinal interneuron phase-cancelling descending oscillations. Therefore, during skill learning, the central nervous system acts as a matched filter, adjusting synaptic weights of shared inputs to suppress neural components unrelated to the specific task.