Power In Frequency Bands Research Articles

Effects of centrally acting analgesics on resting-state electroencephalography biomarker candidates of chronic pain.

Resting-state electroencephalography (rsEEG) holds promise as a biomarker of chronic pain. However, the impact of centrally acting analgesics like opioids, antiepileptics, and antidepressants on rsEEG remains unclear. This confounds and limits the interpretability of previous studies and questions the specificity of rsEEG biomarker candidates of chronic pain, especially for potential diagnostic biomarkers. We, therefore, aimed to elucidate the effects of opioids, antiepileptics, and antidepressants on common rsEEG biomarker candidates of chronic pain. To this end, we analyzed two large, independent rsEEG datasets, including 217 people with chronic pain. We performed preregistered multivariate Bayesian analyses to allow for the quantification and interpretation of evidence in favor of as well as against medication effects on EEG. We specifically evaluated the effects of different centrally acting analgesics on rsEEG features and controlled for the potential confounds of age, pain intensity, and depression. Results predominantly provided evidence against effects of centrally acting analgesics on peak alpha frequency, oscillatory power in different frequency bands, and connectivity-based network measures. Although these findings do not rule out any effects of analgesics on rsEEG, they argue against medium to large effects of centrally acting analgesics on rsEEG. These results help to interpret previous and future rsEEG findings in people with chronic pain and strengthen the validity of rsEEG biomarker candidates of chronic pain. Thus, the present findings can help to develop clinically valuable biomarkers of chronic pain. PERSPECTIVE: This study investigated the effects of centrally acting analgesics on brain-based biomarker candidates of chronic pain, as assessed by electroencephalography. The results predominantly provided evidence against effects on peak alpha frequency, oscillatory power, and connectivity-based network measures. This might help to develop clinically useful biomarkers of chronic pain. DATA AVAILABILITY: Datasets are available at https://osf.io/uzgxw/files/osfstorage.

Effects of Mental Workload Manipulation on Electroencephalography Spectrum Oscillation and Microstates in Multitasking Environments.

Multitasking during flights leads to a high mental workload, which is detrimental for maintaining task performance. Electroencephalography (EEG) power spectral analysis based on frequency-band oscillations and microstate analysis based on global brain network activation can be used to evaluate mental workload. This study explored the effects of a high mental workload during simulated flight multitasking on EEG frequency-band power and microstate parameters. Thirty-six participants performed multitasking with low and high mental workloads after 4 consecutive days of training. Two levels of mental workload were set by varying the number of subtasks. EEG signals were acquired during the task. Power spectral and microstate analyses were performed on the EEG. The indices of four frequency bands (delta, theta, alpha, and beta) and four microstate classes (A-D) were calculated, changes in the frequency-band power and microstate parameters under different mental workloads were compared, and the relationships between the two types of EEG indices were analyzed. The theta-, alpha-, and beta-band powers were higher under the high than under the low mental workload condition. Compared with the low mental workload condition, the high mental workload condition had a lower global explained variance and time parameters of microstate B but higher time parameters of microstate D. Less frequent transitions between microstates A and B and more frequent transitions between microstates C and D were observed during high mental workload conditions. The time parameters of microstate B were positively correlated with the delta-, theta-, and beta-band powers, whereas the duration of microstate C was negatively correlated with the beta-band power. EEG frequency-band power and microstate parameters can be used to detect a high mental workload. Power spectral analyses based on frequency-band oscillations and microstate analyses based on global brain network activation were not completely isolated during multitasking.

Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study.

Noninvasive brain stimulation (NIBS) can boost motor recovery after a stroke. Certain movement phases are more responsive to NIBS, so a system that auto-detects these phases would optimize stimulation timing. This study assessed the effectiveness of various machine learning models in identifying movement phases in hemiparetic individuals undergoing simultaneous NIBS and EEG recordings. We hypothesized that transcranial direct current stimulation (tDCS), a form of NIBS, would enhance EEG signals related to movement phases and improve classification accuracy compared to sham stimulation. EEG data from 10 chronic stroke patients and 11 healthy controls were recorded before, during, and after tDCS. Eight machine learning algorithms and five ensemble methods were used to classify two movement phases (hold posture and reaching) during each of these periods. Data preprocessing included z-score normalization and frequency band power binning. In chronic stroke participants who received active tDCS, the classification accuracy for hold vs. reach phases increased from pre-stimulation to the late intra-stimulation period (72.2% to 75.2%, p < 0.0001). Late active tDCS surpassed late sham tDCS classification (75.2% vs. 71.5%, p < 0.0001). Linear discriminant analysis was the most accurate (74.6%) algorithm with the shortest training time (0.9 s). Among ensemble methods, low gamma frequency (30-50 Hz) achieved the highest accuracy (74.5%), although this result did not achieve statistical significance for actively stimulated chronic stroke participants. Machine learning algorithms showed enhanced movement phase classification during active tDCS in chronic stroke participants. These results suggest their feasibility for real-time movement detection in neurorehabilitation, including brain-computer interfaces for stroke recovery.

Анализ электрической активности различных областей головного мозга для оценки различий функциональных состояний человека

The study presents an evaluation of the feasibility of using various EEG leads for calculating EEG signal metrics aimed at detecting differences in the EEG signal of a person in a state of concentration and a state of mind-wandering. The leads and metrics were selected based on an analysis of related studies focused on recognizing and assessing human states, such as concentration. Practical methods for calculating selected power and entropy metrics in various frequency bands are provided. The study uses a unique dataset containing EEG recordings for functional states of concentration at a point in the center of the forehead and background states of mind-wandering, including data from 17 participants. The assessment of the applicability of leads was based on the point-biserial correlation coefficient of the metric values with functional states, as well as a similar measure based on the difference in interquartile ranges of the metric values. It was shown that the most promising leads for application are prefrontal leads for calculating the signal entropy metric in the 0.3 to 30 Hz range, and occipital leads for calculating spectral power metrics in the α and θ ranges. For several metrics, not only absolute values are important, but also their ratios for different brain regions (prefrontal, central, and occipital). Among the relative values, the most promising is the ratio of metric values in the prefrontal leads to the corresponding values in the occipital leads. These metrics on the selected leads show significant but divergent changes between functional states for the participants in the sample.

Improved empirical wavelet transform combined with particle swarm optimization-support vector machine for EEG-based depression recognition

Depression not only inflicts physical harm on patients and diminishes their quality of life, but also imposes a significant burden on families and society. Current diagnostic methods are predominantly employed post-onset, leading to a lack of early intervention opportunities for patients. Therefore, there is a pressing need to develop techniques for detecting early signs of depression to enable timely intervention and potentially improve recovery rates. In this paper, we propose an improved method for the early objective diagnosis of depression utilizing an empirical wavelet transform (EWT) technique enhanced by a particle swarm optimization-support vector machine (PSO-SVM) algorithm. Our approach specifically focuses on the Fpz channel in the prefrontal lobe of the brain, which most accurately reflects the electrical anomalies associated with depression among 128 channels of resting-state electroencephalogram (EEG). The EWT is refined based on the Morlet wavelet, which allows for the precise decomposition of EEG rhythms. From these decompositions, we effectively extract six depression-related EEG features: frequency band power, frequency band power ratio, Shannon entropy, permutation entropy, LZ complexity, and variance. Afterward, these distinguishing characteristics are harnessed to detect depression through the optimized PSO-SVM algorithm. Our approach exhibited a accuracy rate of 81.25% on the MODMA publicly accessible dataset, thereby validating its proficiency in assisting in the diagnosis of depression via the analysis of the EEG Alpha band.

Discovering EEG biomarkers of Lennox-Gastaut syndrome through unsupervised time-frequency analysis.

The discovery and validation of electroencephalography (EEG) biomarkers often rely on visual identification of waveforms. However, bias toward visually striking events restricts the search space for new biomarkers, and low interrater reliability can limit rigorous validation. We present a data-driven approach to biomarker discovery called scalp EEG Pattern Identification and Categorization (s-EPIC), which enables automated, unsupervised identification of EEG waveforms. S-EPIC is validated on Lennox-Gastaut syndrome (LGS), an epilepsy that is difficult to diagnose and assess due to its variable presentation and insidious evolution of symptoms. We retrospectively collected 10-min scalp EEG clips during non-rapid eye movement (NREM) sleep from 20 subjects with LGS and 20 approximately age-matched healthy controls. For s-EPIC, EEG events of interest (EOIs) were detected in all subjects using time-frequency analysis. The 11 705 EOIs were characterized based on 11 features and were collectively grouped using both k-means clustering and feature categorization. To provide clinical context, 1350 EOIs were visually reviewed and classified by three epileptologists. s-EPIC identified four clusters as candidate biomarkers of LGS, each having significantly more LGS EOIs than control EOIs. Two clusters contained EOIs resembling known LGS biomarkers such as interictal epileptiform discharges and generalized paroxysmal fast activity. The other two LGS-associated EEG clusters contained short bursts of power in beta and gamma frequency bands that were primarily unrecognized by epileptologists. This approach also uncovered significant differences in sleep spindles between LGS and control cohorts. s-EPIC provides a quantitative approach to waveform identification that could be broadly applied to EEG from both healthy subjects and those with suspected pathology. s-EPIC can objectively identify and characterize relevant EEG waveforms without visual review or assumptions about the waveform's morphology and could therefore be a powerful tool for the discovery and refinement of EEG biomarkers.

3D convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery EEG signal

Non-invasive brain-computer interfaces (BCI) hold great promise in the field of neurorehabilitation. They are easy to use and do not require surgery, particularly in the area of motor imagery electroencephalography (EEG). However, motor imagery EEG signals often have a low signal-to-noise ratio and limited spatial and temporal resolution. Traditional deep neural networks typically only focus on the spatial and temporal features of EEG, resulting in relatively low decoding and accuracy rates for motor imagery tasks. To address these challenges, this paper proposes a 3D Convolutional Neural Network (P-3DCNN) decoding method that jointly learns spatial-frequency feature maps from the frequency and spatial domains of the EEG signals. First, the Welch method is used to calculate the frequency band power spectrum of the EEG, and a 2D matrix representing the spatial topology distribution of the electrodes is constructed. These spatial-frequency representations are then generated through cubic interpolation of the temporal EEG data. Next, the paper designs a 3DCNN network with 1D and 2D convolutional layers in series to optimize the convolutional kernel parameters and effectively learn the spatial-frequency features of the EEG. Batch normalization and dropout are also applied to improve the training speed and classification performance of the network. Finally, through experiments, the proposed method is compared to various classic machine learning and deep learning techniques. The results show an average decoding accuracy rate of 86.69%, surpassing other advanced networks. This demonstrates the effectiveness of our approach in decoding motor imagery EEG and offers valuable insights for the development of BCI.

Functional network disruption in cognitively unimpaired autosomal dominant Alzheimer's disease: a magnetoencephalography study.

Understanding the nature and onset of neurophysiological changes, and the selective vulnerability of central hub regions in the functional network, may aid in managing the growing impact of Alzheimer's disease on society. However, the precise neurophysiological alterations occurring in the pre-clinical stage of human Alzheimer's disease remain controversial. This study aims to provide increased insights on quantitative neurophysiological alterations during a true early stage of Alzheimer's disease. Using high spatial resolution source-reconstructed magnetoencephalography, we investigated regional and whole-brain neurophysiological changes in a unique cohort of 11 cognitively unimpaired individuals with pathogenic mutations in the presenilin-1 or amyloid precursor protein gene and a 1:3 matched control group (n = 33) with a median age of 49 years. We examined several quantitative magnetoencephalography measures that have been shown robust in detecting differences in sporadic Alzheimer's disease patients and are sensitive to excitation-inhibition imbalance. This includes spectral power and functional connectivity in different frequency bands. We also investigated hub vulnerability using the hub disruption index. To understand how magnetoencephalography measures change as the disease progresses through its pre-clinical stage, correlations between magnetoencephalography outcomes and various clinical variables like age were analysed. A comparison of spectral power between mutation carriers and controls revealed oscillatory slowing, characterized by widespread higher theta (4-8 Hz) power, a lower posterior peak frequency and lower occipital alpha 2 (10-13 Hz) power. Functional connectivity analyses presented a lower whole-brain (amplitude-based) functional connectivity in the alpha (8-13 Hz) and beta (13-30 Hz) bands, predominantly located in parieto-temporal hub regions. Furthermore, we found a significant hub disruption index for (phase-based) functional connectivity in the theta band, attributed to both higher functional connectivity in 'non-hub' regions alongside a hub disruption. Neurophysiological changes did not correlate with indicators of pre-clinical disease progression in mutation carriers after multiple comparisons correction. Our findings provide evidence that oscillatory slowing and functional connectivity differences occur before cognitive impairment in individuals with autosomal dominant mutations leading to early onset Alzheimer's disease. The nature and direction of these alterations are comparable to those observed in the clinical stages of Alzheimer's disease, suggest an early excitation-inhibition imbalance, and fit with the activity-dependent functional degeneration hypothesis. These insights may prove useful for early diagnosis and intervention in the future.

Heart Rate Variability for Supplementing Withdrawal Assessment in Patients with Opioid Dependence: An Exploratory Study.

Management of opioid dependence requires titrating medication doses based on withdrawal symptoms, but its clinical assessment presents challenges when it comes to subjective reporting. This study aimed to find out the relationship between heart rate variability (HRV) and opioid withdrawal in patients with opioid dependence. Three groups of adult males were recruited: (a) patients with opioid dependence undergoing inpatient detoxification, (b) patients with opioid dependence stabilized on buprenorphine-based opioid substitution treatment, and (c) healthy controls. Frequency and time-domain parameters of HRV were used in the analysis. The opioid withdrawal was assessed using the Subjective Opiate Withdrawal Scale (SOWS). Resting heart rate was found to be significantly different across the three groups (higher in patients stabilized on buprenorphine than the other two groups). In time-domain parameters, the detoxification group had the highest beat-to-beat variability. In frequency-domain parameters, the total power was highest for the detoxification group and lowest for the opioid substitution treatment group. In contrast, the relative power of frequency bands (very low, low, and high) did not vary across the groups at baseline. The SOWS had a weak negative correlation with root mean square of successive differences (RMSSD) in the opioid substitution group and did not have any relationship with HRV parameters in the detoxification group. This exploratory study did not find HRV parameters to be robustly associated with subjective withdrawal, except for a negative association with the beat-to-beat variability among patients on opioid substitution treatment. This study adds to information on HRV in patients with opioid dependence.

State-Dependent Motor Cortex Stimulation Reveals Distinct Mechanisms for Corticospinal Excitability and Cortical Responses.

Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation method that modulates brain activity by inducing electric fields in the brain. Real-time, state-dependent stimulation with TMS has shown that neural oscillation phase modulates corticospinal excitability. However, such motor evoked potentials (MEPs) only indirectly reflect motor cortex activation and are unavailable at other brain regions of interest. The direct and secondary cortical effects of phase-dependent brain stimulation remain an open question. In this study, we recorded the cortical responses during single-pulse TMS using electroencephalography (EEG) concurrently with the MEP measurements in 20 healthy human volunteers (11 female). TMS was delivered at peak, rising, trough, and falling phases of mu (8-13 Hz) and beta (14-30 Hz) oscillations in the motor cortex. The cortical responses were quantified through TMS evoked potential components N15, P50, and N100 as peak-to-peak amplitudes (P50-N15 and P50-N100). We further analyzed whether the prestimulus frequency band power was predictive of the cortical responses. We demonstrated that phase-specific targeting modulates cortical responses. The phase relationship between cortical responses was different for early and late responses. In addition, pre-TMS mu oscillatory power and phase significantly predicted both early and late cortical EEG responses in mu-specific targeting, indicating the independent causal effects of phase and power. However, only pre-TMS beta power significantly predicted the early and late TEP components during beta-specific targeting. Further analyses indicated distinct roles of mu and beta power on cortical responses. These findings provide insight to mechanistic understanding of neural oscillation states in cortical and corticospinal activation in humans.

EEG biomarkers in Alzheimer’s and prodromal Alzheimer’s: a comprehensive analysis of spectral and connectivity features

BackgroundBiomarkers of Alzheimer’s disease (AD) and mild cognitive impairment (MCI, or prodromal AD) are highly significant for early diagnosis, clinical trials and treatment outcome evaluations. Electroencephalography (EEG), being noninvasive and easily accessible, has recently been the center of focus. However, a comprehensive understanding of EEG in dementia is still needed. A primary objective of this study is to investigate which of the many EEG characteristics could effectively differentiate between individuals with AD or prodromal AD and healthy individuals.MethodsWe collected resting state EEG data from individuals with AD, prodromal AD, and normal cognition. Two distinct preprocessing pipelines were employed to study the reliability of the extracted measures across different datasets. We extracted 41 different EEG features. We have also developed a stand-alone software application package, Feature Analyzer, as a comprehensive toolbox for EEG analysis. This tool allows users to extract 41 EEG features spanning various domains, including complexity measures, wavelet features, spectral power ratios, and entropy measures. We performed statistical tests to investigate the differences in AD or prodromal AD from age-matched cognitively normal individuals based on the extracted EEG features, power spectral density (PSD), and EEG functional connectivity.ResultsSpectral power ratio measures such as theta/alpha and theta/beta power ratios showed significant differences between cognitively normal and AD individuals. Theta power was higher in AD, suggesting a slowing of oscillations in AD; however, the functional connectivity of the theta band was decreased in AD individuals. In contrast, we observed increased gamma/alpha power ratio, gamma power, and gamma functional connectivity in prodromal AD. Entropy and complexity measures after correcting for multiple electrode comparisons did not show differences in AD or prodromal AD groups. We thus catalogued AD and prodromal AD-specific EEG features.ConclusionsOur findings reveal that the changes in power and connectivity in certain frequency bands of EEG differ in prodromal AD and AD. The spectral power, power ratios, and the functional connectivity of theta and gamma could be biomarkers for diagnosis of AD and prodromal AD, measure the treatment outcome, and possibly a target for brain stimulation.

Effect of fatigue on intermuscular EMG-EMG coupling during bench press exercise at 60% 1RM workload in males.

To explore the neuromuscular control mechanism and quantifying the fatigue response during bench press exercise is important references to prescribe an appropriate exercise program. However, current literature struggles to provide a concrete conclusion on the changes of intermuscular EMG-EMG coupling between synergistic and antagonist muscles during the exercise. Thus, the current study was designed to reveal fatigue-related changes of intermuscular EMG-EMG coupling during bench press exercise. Thirty-one healthy male participants performed a bench press exercise on the Smith machine at 60% One Repetition Maximum (1RM) workload to exhaustion, while surface electromyographic signals (sEMG) were collected from triceps brachii (TB), biceps brachii (BB), anterior deltoid (AD), posterior deltoid (PD), and pectoralis major (PM). Surface EMG signals were divided into the first half and second half of the bench press exercise. Phase synchronization index (PSI) was calculated between sEMG of synergistic muscle pairs AD-TB, AD-PM and antagonist muscle pairs BB-TB, AD-PD. EMG power of TB, AD, PD, PM muscles in alpha (8-12 Hz) frequency band and EMG power of each muscle in beta (15-35 Hz), and gamma (35-60 Hz) frequency bands were all increased during the second half of contraction compared with the first half of contraction. PSI of gamma frequency band was significantly decreased in BB-TB muscle pair while EMG-EMG coupling of AD-TB in gamma frequency band was significantly increased during the second half of contraction compared to the first half of contraction. The results indicated a decrease of interconnection between synchronized cortical neurons and the motoneuron pool of BB and TB, and an increase of interconnection between AD-TB muscles during fatiguing bench press exercise at 60% 1RM workload. The changes of intermuscular coupling may be related to the supraspinal modulations to compensate for the decrease of muscle force as well as a result of unbalanced changes of agonist and antagonist muscle contractility.

DO NOT LOSE SLEEP OVER IT: IMPLANTED BRAIN-COMPUTER INTERFACE FUNCTIONALITY DURING NIGHTTIME IN LATE-STAGE AMYOTROPHIC LATERAL SCLEROSIS.

Brain-computer interfaces (BCIs) hold promise as augmentative and alternative communication technology for people with severe motor and speech impairment (locked-in syndrome) due to neural disease or injury. Although such BCIs should be available 24/7, to enable communication at all times, feasibility of nocturnal BCI use has not been investigated. Here, we addressed this question using data from an individual with amyotrophic lateral sclerosis (ALS) who was implanted with an electrocorticography-based BCI that enabled the generation of click-commands for spelling words and call-caregiver signals. We investigated nocturnal dynamics of neural signal features used for BCI control, namely low (LFB: 10-30Hz) and high frequency band power (HFB: 65-95Hz). Additionally, we assessed the nocturnal performance of a BCI decoder that was trained on daytime data by quantifying the number of unintentional BCI activations at night. Finally, we developed and implemented a nightmode decoder that allowed the participant to call a caregiver at night, and assessed its performance. Power and variance in HFB and LFB were significantly higher at night than during the day in the majority of the nights, with HFB variance being higher in 88% of nights. Daytime decoders caused 245 unintended selection-clicks and 13 unintended caregiver-calls per hour when applied to night data. The developed nightmode decoder functioned error-free in 79% of nights over a period of ±1.5 years, allowing the user to reliably call the caregiver, with unintended activations occurring only once every 12 nights. Reliable nighttime use of a BCI requires decoders that are adjusted to sleep-related signal changes. This demonstration of a reliable BCI nightmode and its long-term use by an individual with advanced ALS underscores the importance of 24/7 BCI reliability. This trial is registered in clinicaltrials.gov under number NCT02224469 (https://clinicaltrials.gov/study/NCT02224469?term=NCT02224469&rank=1). Date of submission to registry: August 21, 2014. Enrollment of first participant: September 7, 2015.

A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data.

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a source of potential biomarkers for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. This paper proposes a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address non-stationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Compared to previous mixed-effects state-space models, we directly model high-dimensional random effects matrices of interest without structural constraints and tackle the challenge of identifiability. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of major depressive disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals. In addition, we show the subject-level EEG features derived from RESSM exhibit a superior predictive value for the heterogeneous treatment effect compared to the EEG frequency band power, suggesting the potential of EEG as a valuable biomarker for MDD.

Altered Resting-State Electroencephalogram Microstate Characteristics in Stroke Patients.

Stroke remains a leading cause of disability globally and movement impairment is the most common complication in stroke patients. Resting-state electroencephalography (EEG) microstate analysis is a non-invasive approach of whole-brain imaging based on the spatiotemporal pattern of the entire cerebral cortex. The present study aims to investigate microstate alterations in stroke patients. Resting-state EEG data collected from 24 stroke patients and 19 healthy controls matched by age and gender were subjected to microstate analysis. For four classic microstates labeled as class A, B, C and D, their temporal characteristics (duration, occurrence and coverage) and transition probabilities (TP) were extracted and compared between the two groups. Furthermore, we explored their correlations with clinical outcomes including the Fugl-Meyer assessment (FMA) and the action research arm test (ARAT) scores in stroke patients. Finally, we analyzed the relationship between the temporal characteristics and spectral power in frequency bands. False discovery rate (FDR) method was applied for correction of multiple comparisons. Microstate analysis revealed that the stroke group had lower occurrence of microstate A which was regarded as the sensorimotor network (SMN) compared with the control group (p = 0.003, adjusted p = 0.036, t = -2.959). The TP from microstate A to microstate D had a significant positive correlation with the Fugl-Meyer assessment of lower extremity (FMA-LE) scores (p = 0.049, r = 0.406), but this finding did not survive FDR adjustment (adjusted p = 0.432). Additionally, the occurrence and the coverage of microstate B were negatively correlated with the power of delta band in the stroke group, which did not pass adjustment (p = 0.033, adjusted p = 0.790, r = -0.436; p = 0.026, adjusted p = 0.790, r = -0.454, respectively). Our results confirm the abnormal temporal dynamics of brain activity in stroke patients. The study provides further electrophysiological evidence for understanding the mechanism of brain motor functional reorganization after stroke.

Machine learning based classification of presence utilizing psychophysiological signals in immersive virtual environments

In Virtual Reality (VR), a higher level of presence positively influences the experience and engagement of a user. There are several parameters that are responsible for generating different levels of presence in VR, including but not limited to, graphical fidelity, multi-sensory stimuli, and embodiment. However, standard methods of measuring presence, including self-reported questionnaires, are biased. This research focuses on developing a robust model, via machine learning, to detect different levels of presence in VR using multimodal neurological and physiological signals, including electroencephalography and electrodermal activity. An experiment has been undertaken whereby participants (N = 22) were each exposed to three different levels of presence (high, medium, and low) in a random order in VR. Four parameters within each level, including graphics fidelity, audio cues, latency, and embodiment with haptic feedback, were systematically manipulated to differentiate the levels. A number of multi-class classifiers were evaluated within a three-class classification problem, using a One-vs-Rest approach, including Support Vector Machine, k-Nearest Neighbour, Extra Gradient Boosting, Random Forest, Logistic Regression, and Multiple Layer Perceptron. Results demonstrated that the Multiple Layer Perceptron model obtained the highest macro average accuracy of 93±0.03%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93\\pm 0.03\\%$$\\end{document}. Posthoc analysis revealed that relative band power, which is expressed as the ratio of power in a specific frequency band to the total baseline power, in both the frontal and parietal regions, including beta over theta and alpha ratio, and differential entropy were most significant in detecting different levels of presence.

Fronto-Central Changes in Multiple Frequency Bands in Active Tactile Width Discrimination Task.

The neural basis of tactile processing in humans has been extensively studied; however, the neurophysiological basis of human width discrimination remains relatively unexplored. In particular, the changes that occur in neural networks underlying active tactile width discrimination learning have yet to be described. Here, it is hypothesized that subjects learning to perform the active version of the width discrimination task would present changes in behavioral data and in the neurophysiological activity, specifically in networks of electrodes relevant for tactile and motor processing. The specific hypotheses tested here were that the performance and response latency of subjects would change between the first and the second blocks; the power of the different frequency bands would change between the first and the second blocks; electrode F4 would encode task performance and response latency through changes in the power of the delta, theta, alpha, beta, and low-gamma frequency bands; the relative power in the alpha and beta frequency bands in electrodes C3 and C4 (Interhemispheric Spectral Difference-ISD) would change because of learning between the first and the second blocks. To test this hypothesis, we recorded and analyzed electroencephalographic (EEG) activity while subjects performed a session where they were tested twice (i.e., two different blocks) in an active tactile width discrimination task using their right index finger. Subjects (n = 18) presented high performances (high discrimination accuracy) already in their first block, and therefore no significant improvements were found in the second block. Meanwhile, a reduction in response latency was observed between the two blocks. EEG recordings revealed an increase in power for the low-gamma frequency band (30-45 Hz) for electrodes F3 and C3 from the first to the second block. This change was correlated with neither performance nor latency. Analysis of the neural activity in electrode F4 revealed that the beta frequency band encoded the subjects' performance. Meanwhile, the delta frequency band in the same electrode revealed a complex pattern where blocks appeared clustered in two different patterns: an Upper Pattern (UP), where power and latency were highly correlated (Rho = 0.950), and a sparser and more uncorrelated Lower Pattern (LP). Blocks belonging to the UP or LP patterns did not differ in performance and were not specific to the first or the second block. However, blocks belonging to the LP presented an increase in response latency, increased variability in performance, and an increased ISD in alpha and beta frequency bands for the pair of electrodes C3-C4, suggesting that the LP may reflect a state related to increased cognitive load or task difficulty. These results suggest that changes in performance and latency in an active tactile width discrimination task are encoded in the delta, alpha, beta, and low-gamma frequency bands in a fronto-central network. The main contribution of this study is therefore related to the description of neural dynamics in frontal and central networks involved in the learning process of active tactile width discrimination.

Specific EEG resting state biomarkers in FXS and ASD

BackgroundFragile X syndrome (FXS) and autism spectrum disorder (ASD) are neurodevelopmental conditions that often have a substantial impact on daily functioning and quality of life. FXS is the most common cause of inherited intellectual disability (ID) and the most common monogenetic cause of ASD. Previous literature has shown that electrophysiological activity measured by electroencephalogram (EEG) during resting state is perturbated in FXS and ASD. However, whether electrophysiological profiles of participants with FXS and ASD are similar remains unclear. The aim of this study was to compare EEG alterations found in these two clinical populations presenting varying degrees of cognitive and behavioral impairments.MethodsResting state EEG signal complexity, alpha peak frequency (APF) and power spectral density (PSD) were compared between 47 participants with FXS (aged between 5–20), 49 participants with ASD (aged between 6–17), and 52 neurotypical (NT) controls with a similar age distribution using MANCOVAs with age as covariate when appropriate. MANCOVAs controlling for age, when appropriate, and nonverbal intelligence quotient (NVIQ) score were subsequently performed to determine the impact of cognitive functioning on EEG alterations.ResultsOur results showed that FXS participants manifested decreased signal complexity and APF compared to ASD participants and NT controls, as well as altered power in the theta, alpha and low gamma frequency bands. ASD participants showed exaggerated beta power compared to FXS participants and NT controls, as well as enhanced low and high gamma power compared to NT controls. However, ASD participants did not manifest altered signal complexity or APF. Furthermore, when controlling for NVIQ, results of decreased complexity in higher scales and lower APF in FXS participants compared to NT controls and ASD participants were not replicated.ConclusionsThese findings suggest that signal complexity and APF might reflect cognitive functioning, while altered power in the low gamma frequency band might be associated with neurodevelopmental conditions, particularly FXS and ASD.

EEG Functional Connectivity Differences Predict Future Conversion to Dementia in Mild Cognitive Impairment With Lewy Body or Alzheimer Disease.

Predicting which individuals may convert to dementia from mild cognitive impairment (MCI) remains difficult in clinical practice. Electroencephalography (EEG) is a widely available investigation but there is limited research exploring EEG connectivity differences in patients with MCI who convert to dementia. Participants with a diagnosis of MCI due to Alzheimer's disease (MCI-AD) or Lewy body disease (MCI-LB) underwent resting state EEG recording. They were followed up annually with a review of the clinical diagnosis (n=66). Participants with a diagnosis of dementia at year 1 or year 2 follow up were classed as converters (n=23) and those with a diagnosis of MCI at year 2 were classed as stable (n=43). We used phase lag index (PLI) to estimate functional connectivity as well as analysing dominant frequency (DF) and relative band power. The Network-based statistic (NBS) toolbox was used to assess differences in network topology. The converting group had reduced DF (U=285.5, p=0.005) and increased relative pre-alpha power (U=702, p=0.005) consistent with previous findings. PLI showed reduced average beta band synchrony in the converting group (U=311, p=0.014) as well as significant differences in alpha and beta network topology. Logistic regression models using regional beta PLI values revealed that right central to right lateral (Sens=56.5%, Spec=86.0%, -2LL=72.48, p=0.017) and left central to right lateral (Sens=47.8%, Spec=81.4%, -2LL=71.37, p=0.012) had the best classification accuracy and fit when adjusted for age and MMSE score. Patients with MCI who convert to dementia have significant differences in EEG frequency, average connectivity and network topology prior to the onset of dementia. The MCI group is clinically heterogeneous and have underlying physiological differences that may be driving the progression of cognitive symptoms. EEG connectivity could be useful to predict which patients with MCI-AD and MCI-LB convert to dementia, regardless of the neurodegenerative aetiology.

Resting-state neural dynamics changes in older adults with post-COVID syndrome and the modulatory effect of cognitive training and sex.

Post-COVID syndrome manifests with numerous neurological and cognitive symptoms, the precise origins of which are still not fully understood. As females and older adults are more susceptible to developing this condition, our study aimed to investigate how post-COVID syndrome alters intrinsic brain dynamics in older adults and whether biological sex and cognitive training might modulate these effects, with a specific focus on older females. The participants, aged between 60 and 75years, were divided into three experimental groups: healthy old female, post-COVID old female and post-COVID old male. They underwent an adaptive task-switching training protocol. We analysed multiscale entropy and spectral power density of resting-state EEG data collected before and after the training to assess neural signal complexity and oscillatory power, respectively. We found no difference between post-COVID females and males before training, indicating that post-COVID similarly affected both sexes. However, cognitive training was effective only in post-COVID females and not in males, by modulating local neural processing capacity. This improvement was further evidenced by comparing healthy and post-COVID females, wherein the latter group showed increased finer timescale entropy (1-30ms) and higher frequency band power (11-40Hz) before training, but these differences disappeared following cognitive training. Our results suggest that in older adults with post-COVID syndrome, there is a pronounced shift from more global to local neural processing, potentially contributing to accelerated neural aging in this condition. However, cognitive training seems to offer a promising intervention method for modulating these changes in brain dynamics, especially among females.