EEG Dynamics Research Articles

Motor-Related EEG Analysis Using a Pole Tracking Approach.

This study introduces an alternative approach to electroencephalography (EEG) time-frequency analysis based on time-varying autoregressive (TV-AR) models in a cascade configuration to independently monitor key EEG spectral components. The method is evaluated for its neurophysiological interpretation and effectiveness in motor-related brain-computer interface (BCI) applications. Specifically, we assess the ability of the tracked EEG poles to discriminate between rest, movement execution (ME) and movement imagination (MI) in healthy subjects, as well as movement attempts (MA) in individuals with spinal cord injury (SCI). Our results show that pole tracking effectively captures broad changes in EEG dynamics, such as transitions between rest and movement-related states. It outperformed traditional EEG-based features, increasing detection accuracy for ME by an average of 4.1% (with individual improvements reaching as high as 15%) and MI by an average of 4.5% (up to 13.8%) compared to time-domain low-frequency EEG features. Similarly, compared to alpha/beta band power, the method improved ME detection by an average of 5.9% (up to 10.4%) and MI by an average of 4.3% (up to 10.2%), with results averaged across 15 healthy participants. In one participant with SCI, pole tracking improved MA detection by 12.9% over low-frequency EEG features and 4.8% over alpha/beta band power. However, its ability to distinguish finer movement details within specific movement types was limited. Additionally, the temporal evolution of the extracted pole tracking features revealed event-related desynchronization phenomena, typically observed during ME, MA and MI, as well as increases in frequency, which are of neurophysiological interest.

ID: 325336 Examining the Impact of Transcranial Magnetic Stimulation Intensity on Instantaneous Communication with dynamical EEG analysis

ID: 325336 Examining the Impact of Transcranial Magnetic Stimulation Intensity on Instantaneous Communication with dynamical EEG analysis

Motion Artifacts in Dynamic EEG Recordings: Experimental Observations, Electrical Modelling, and Design Considerations

Despite the progress in the development of innovative EEG acquisition systems, their use in dynamic applications is still limited by motion artifacts compromising the interpretation of the collected signals. Therefore, extensive research on the genesis of motion artifacts in EEG recordings is still needed to optimize existing technologies, shedding light on possible solutions to overcome the current limitations. We identified three potential sources of motion artifacts occurring at three different levels of a traditional biopotential acquisition chain: the skin-electrode interface, the connecting cables between the detection and the acquisition systems, and the electrode-amplifier system. The identified sources of motion artifacts were modelled starting from experimental observations carried out on EEG signals. Consequently, we designed customized EEG electrode systems aiming at experimentally disentangling the possible causes of motion artifacts. Both analytical and experimental observations indicated two main residual sites responsible for motion artifacts: the connecting cables between the electrodes and the amplifier and the sudden changes in electrode-skin impedance due to electrode movements. We concluded that further advancements in EEG technology should focus on the transduction stage of the biopotentials amplification chain, such as the electrode technology and its interfacing with the acquisition system.

Chrono-EEG dynamics influencing hand gesture decoding: a 10-hour study

Long-term electroencephalography (EEG) recordings have primarily been used to study resting-state fluctuations. These recordings provide valuable insights into various phenomena such as sleep stages, cognitive processes, and neurological disorders. However, this study explores a new angle, focusing for the first time on the evolving nature of EEG dynamics over time within the context of movement. Twenty-two healthy individuals were measured six times from 2 p.m. to 12 a.m. with intervals of 2 h while performing four right-hand gestures. Analysis of movement-related cortical potentials (MRCPs) revealed a reduction in amplitude for the motor and post-motor potential during later hours of the day. Evaluation in source space displayed an increase in the activity of M1 of the contralateral hemisphere and the SMA of both hemispheres until 8 p.m. followed by a decline until midnight. Furthermore, we investigated how changes over time in MRCP dynamics affect the ability to decode motor information. This was achieved by developing classification schemes to assess performance across different scenarios. The observed variations in classification accuracies over time strongly indicate the need for adaptive decoders. Such adaptive decoders would be instrumental in delivering robust results, essential for the practical application of BCIs during day and nighttime usage.

Development of EEG Response to Unpleasant, Pleasant, and Neutral Tactile Stimuli.

The study of tactile perception during a childhood is extremely important for understanding the social and communicative aspects of the child's development. Tactile perception of stimuli with different valence can have different normative stages of development. In the present study, we examined changes in linear and nonlinear electroencephalogram (EEG) parameters during the presentation of pleasant (C-tactile optimal stroking), unpleasant (ice stroking), and neutral tactile stimuli in three groups of healthy volunteers: preschoolers 4 and 5 years, school-age children from 8 to 10 years, and adults from 20 to 40 years. According to our findings, child maturation plays a significant role in the perception of pleasant and neutral tactile stimuli. Patterns of EEG dynamics related to tactile perception showed greater similarity between adult volunteers and school-aged children than preschoolers. Non-linear EEG parameters such as fractal dimension (FD), envelope mean frequency (EMF), and power spectral density (PSD) dynamics of the theta-rhythm were particularly sensitive to developmental changes in tactile perception. Hjorth complexity and peak alpha frequency (PAF) scores may serve as indicators of mature perception of С-tactile (CT)-stimuli.

Critical dynamics in spontaneous EEG predict anesthetic-induced loss of consciousness and perturbational complexity

Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits adaptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigate dynamical properties of the resting-state electroencephalogram (EEG) of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. Importantly, all participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams), enabling an experimental dissociation between unresponsiveness and unconsciousness. For each condition, we measure (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related metrics, revealing that states of unconsciousness are characterized by a distancing from both avalanche criticality and the edge of chaos. We then ask whether these same dynamical properties are predictive of the perturbational complexity index (PCI), a TMS-based measure that has shown remarkably high sensitivity in detecting consciousness independently of behavior. We successfully predict individual subjects’ PCI values with considerably high accuracy from resting-state EEG dynamical properties alone. Our results establish a firm link between perturbational complexity and criticality, and provide further evidence that criticality is a necessary condition for the emergence of consciousness.

Dementia rhythms: Unveiling the EEG dynamics for MCI detection through spectral and synchrony neuromarkers

BackgroundNeurological disorders arise primarily from the dysfunction of brain cells, leading to various impairments. Electroencephalography (EEG) stands out as the most popular method in the discovery of neuromarkers indicating neurological disorders. The proposed study investigates the effectiveness of spectral and synchrony neuromarkers derived from resting state EEG in the detection of Mild Cognitive Impairment (MCI) with controls. New methodsThe dataset is composed of 10 MCI and 10 HC groups. Spectral features and synchrony measures are utilized to detect slowing patterns in MCI. Efficient neuro-markers are classified by 25 classification algorithm. Independent samples t-test and Pearson’s Correlation Coefficients are applied to reveal group differences for spectral markers, and repeated measures ANOVA is tested for wPLI-based markers. ResultsLower peak amplitudes are prominent in MCI participants for high frequencies indicating slower physiological behavior of the demented EEG. The MCI and HC groups are correctly classified with 95 % acc. using peak amplitudes of beta band with LGBM classifier. Higher wPLI values are calculated for HC participants in high frequencies. The alpha wPLI values achieve a classification accuracy of 99 % using the LGBM algorithm for MCI detection. Comparison with existing methodsThe neuro-markers including peak amplitudes, frequencies, and wPLIs with advanced machine learning techniques showcases the innovative nature of this research. ConclusionThe findings suggest that peak amplitudes and wPLI in high frequency bands derived from resting state EEG are effective neuromarkers for detection of MCI. Spectral and synchrony neuro-markers hold great promise for accurate MCI detection.

LGGNet: Learning From Local-Global-Graph Representations for Brain-Computer Interface.

Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose local-global-graph network (LGGNet), a novel neurologically inspired graph neural network (GNN), to learn local-global-graph (LGG) representations of electroencephalography (EEG) for brain-computer interface (BCI). The input layer of LGGNet comprises a series of temporal convolutions with multiscale 1-D convolutional kernels and kernel-level attentive fusion. It captures temporal dynamics of EEG which then serves as input to the proposed local- and global-graph-filtering layers. Using a defined neurophysiologically meaningful set of local and global graphs, LGGNet models the complex relations within and among functional areas of the brain. Under the robust nested cross-validation settings, the proposed method is evaluated on three publicly available datasets for four types of cognitive classification tasks, namely the attention, fatigue, emotion, and preference classification tasks. LGGNet is compared with state-of-the-art (SOTA) methods, such as DeepConvNet, EEGNet, R2G-STNN, TSception, regularized graph neural network (RGNN), attention-based multiscale convolutional neural network-dynamical graph convolutional network (AMCNN-DGCN), hierarchical recurrent neural network (HRNN), and GraphNet. The results show that LGGNet outperforms these methods, and the improvements are statistically significant ( ) in most cases. The results show that bringing neuroscience prior knowledge into neural network design yields an improvement of classification performance. The source code can be found at https://github.com/yi-ding-cs/LGG.

Analysis of EEG Fluctuation Patterns Using Nonlinear Phase-Based Functional Connectivity Measures for Emotion Recognition

Automated emotion recognition is crucial in identifying and monitoring psychological disorders. Although several electroencephalography (EEG)-based methods have been explored for emotion recognition, capturing the subtle oscillations within EEG signals associated with distinct emotional states remains a persistent challenge. Nonlinear phase-based functional connectivity (FC) can capture the intricate time-varying patterns of brain activity during the processing of emotions. In this work, an attempt has been made to characterize the EEG-based emotional states using nonlinear phase-based FC techniques. For this, the EEG signals are obtained from the publicly available DEAP database and decomposed into four frequency bands: Theta (4–7[Formula: see text]Hz), alpha (8–12[Formula: see text]Hz), beta (13–30[Formula: see text]Hz) and gamma (30–45[Formula: see text]Hz). Three nonlinear phase-based FC measures, namely phase lag index (PLI), phase locking value (PLV) and Rho index, are extracted from individual frequency bands. Two types of features, namely network features and FC indices, are fed to three classifiers, namely random forest (RF), extreme gradient boosting (XGB) and K-Nearest Neighbors (KNN). The results reveal that the proposed approach can capture EEG dynamics to characterize emotional states. The gamma band-based Rho index demonstrated prominence in discriminating arousal and valence emotional states. The utilization of the Rho index-based FC feature effectively reveals interactions among cortical brain regions in response to audio-visual stimuli. Thus, the proposed approach could be extended to classifying various emotional states in clinical settings.

Assessing the impact of repetitive transcranial magnetic stimulation on effective connectivity in autism spectrum disorder: An initial exploration using TMS-EEG analysis

Initial indications propose that repetitive transcranial magnetic stimulation (rTMS) could mitigate clinical manifestations in patients with autism spectrum disorder (ASD). Nevertheless, the precise mechanisms responsible for these therapeutic and behavioral outcomes remain elusive. We examined alterations in effective connectivity induced by rTMS using concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) in children with ASD. TMS-EEG data were acquired from 12 children diagnosed with ASD both before and following rTMS treatment. The rTMS intervention regimen included delivering 5-s trains at a frequency of 15 Hz, with 10-min intervals between trains, targeting the left parietal lobe. This was conducted on each consecutive weekday over 3 weeks, totaling 15 sessions. The dynamic EEG network analysis revealed that following the rTMS intervention, long-range feedback connections within the brains of ASD patients were strengthened (e.g., frontal to parietal regions, frontal to occipital regions, and frontal to posterior temporal regions), and short-range connections were weakened (e.g., between the bilateral occipital regions, and between the occipital and posterior temporal regions). In alignment with alterations in network connectivity, there was a corresponding amelioration in fundamental ASD symptoms, as assessed through clinical scales post-treatment. According to our findings, people with ASD may have increased long-range frontal-posterior feedback connection on application of rTMS to the parietal lobe.

Functionally annotated electrophysiological neuromarkers of healthy ageing and memory function.

The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non-reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi-modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in the alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain-specific electrophysiological mechanisms underlying memory performance and ageing. HIGHLIGHTS: We provide an open and reproducible pipeline with a comprehensive workflow to investigate static and dynamic EEG neuromarkers. Neuromarkers related to neural dynamics are sensitive and robust. Individualised alpha power characterises cognitive performance rather than ageing. Functional annotation allows cross-modal interpretation of EEG findings.

Analyzing the States of Brain under Anesthesia Using ANES-metanet based on Deep Learning Models

Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time .Continuous monitoring of electroencephalogram (EEG) recordings in humans under general anesthesia (GA), local anesthesia (LA) and regional anesthesia (RA) has demonstrated that changes in EEG dynamics induced by an anesthetic drug are reliably associated with the altered arousal states caused by the drug. This observation suggests that an intelligent, closed-loop anesthesia delivery (CLAD) system operating in real-time could track EEG dynamics and control the infusion rate of a programmable pump to precisely maintain unconsciousness. In this process we propose a deep learning algorithm to analysis the anesthesia from EEG signal. So the deep learning algorithms are Convolution Neural Network, Long Short Term Neural Network and Artificial Neural Network will applied and analyze EEG signal and it generates the results in the form of metrics like accuracy, precision, recall and f1-score.

EEG analysis in patients with schizophrenia based on microstate semantic modeling method.

Microstate analysis enables the characterization of quasi-stable scalp potential fields on a sub-second timescale, preserving the temporal dynamics of EEG and spatial information of scalp potential distributions. Owing to its capacity to provide comprehensive pathological insights, it has been widely applied in the investigation of schizophrenia (SCZ). Nevertheless, previous research has primarily concentrated on differences in individual microstate temporal characteristics, neglecting potential distinctions in microstate semantic sequences and not fully considering the issue of the universality of microstate templates between SCZ patients and healthy individuals. This study introduced a microstate semantic modeling analysis method aimed at schizophrenia recognition. Firstly, microstate templates corresponding to both SCZ patients and healthy individuals were extracted from resting-state EEG data. The introduction of a dual-template strategy makes a difference in the quality of microstate sequences. Quality features of microstate sequences were then extracted from four dimensions: Correlation, Explanation, Residual, and Dispersion. Subsequently, the concept of microstate semantic features was proposed, decomposing the microstate sequence into continuous sub-sequences. Specific semantic sub-sequences were identified by comparing the time parameters of sub-sequences. The SCZ recognition test was performed on the public dataset for both the quality features and semantic features of microstate sequences, yielding an impressive accuracy of 97.2%. Furthermore, cross-subject experimental validation was conducted, demonstrating that the method proposed in this paper achieves a recognition rate of 96.4% between different subjects. This research offers valuable insights for the clinical diagnosis of schizophrenia. In the future, further studies will seek to augment the sample size to enhance the effectiveness and reliability of this method.

The contribution of EEG to assess and treat motor disorders in multiple sclerosis

ObjectiveElectroencephalography (EEG) can highlight significant changes in spontaneous electrical activity of the brain produced by altered brain network connectivity linked to inflammatory demyelinating lesions and neuronal loss occurring in multiple sclerosis (MS). In this review, we describe the main EEG findings reported in the literature to characterize motor network alteration in term of local activity or functional connectivity changes in patients with MS (pwMS). MethodsA comprehensive literature search was conducted to include articles with quantitative analyses of resting-state EEG recordings (spectrograms or advanced methods for assessing spatial and temporal dynamics, such as coherence, theory of graphs, recurrent quantification, microstates) or dynamic EEG recordings during a motor task, with or without connectivity analyses. ResultsIn this systematic review, we identified 26 original articles using EEG in the evaluation of MS-related motor disorders. Various resting or dynamic EEG parameters could serve as diagnostic biomarkers of motor control impairment to differentiate pwMS from healthy subjects or be related to a specific clinical condition (fatigue) or neuroradiological aspects (lesion load). ConclusionsWe highlight some key EEG patterns in pwMS at rest and during movement, both suggesting an alteration or disruption of brain connectivity, more specifically involving sensorimotor networks. SignificanceSome of these EEG biomarkers of motor disturbance could be used to design future therapeutic strategies in MS based on neuromodulation approaches, or to predict the effects of motor training and rehabilitation in pwMS.

The Dynamics of Resting-State EEG and Salivary Trace Elements in Patients with Obsessive-Compulsive Disorder.

The study of salivary microelements and their neurophysiological and behavioral correlates in patients with obsessive-compulsive disorder (OCD) is a pressing issue in modern psychiatry, which, however, lacks adequate research at this time. In this study, we tested the dynamics of behavioral parameters, resting-state electroencephalogram (EEG), and salivary iron, copper, manganese, magnesium, and zinc in 30 healthy volunteers and 30 individuals with OCD before and after an emotional antisaccade task. The eye-movement data served as a measure of behavioral performance. Our research revealed consistently higher manganese concentrations in the OCD group compared to healthy volunteers associated with a higher EEG ratio of amplitude transformation and symptom severity. The dynamics of salivary microelements and resting-state EEG, possibly influenced by cognitive and emotional load during the anticsaccade task, differed between groups. In healthy volunteers, there was a decrease in salivary iron level with an increase in high-frequency power spectral density of EEG. The OCD group showed a decrease in salivary copper with an increased Hjorth mobility of EEG.

Landau–Kleff ner syndrome

The article presents a long-term catamnestic observation (15 years) of a patient with Landau–Kleff ner syndrome (LCS), in its debut manifested by epileptic paroxysms, total aphasia, cognitive and behavioral impairment, on EEG spike-wave complexes. MRI in epileptic mode was without pathology. He received levetiracetam and hormone therapy (glucocorticosteroids, ACTH analogues) with regression of epileptic seizures and epileptic discharges on EEG and pronounced positive dynamics of speech and other cognitive functions. After 15 years, the patient had a confi dent cognitive defi cit, speech disorders were almost absent.

Beyond peaks and troughs: Multiplexed performance monitoring signals in the EEG.

With the discovery of event-related potentials elicited by errors more than 30 years ago, a new avenue of research on performance monitoring, cognitive control, and decision making emerged. Since then, the field has developed and expanded fulminantly. After a brief overview on the EEG correlates of performance monitoring, this article reviews recent advancements based on single-trial analyses using independent component analysis, multiple regression, and multivariate pattern classification. Given the close interconnection between performance monitoring and reinforcement learning, computational modeling and model-based EEG analyses have made a particularly strong impact. The reviewed findings demonstrate that error- and feedback-related EEG dynamics represent variables reflecting how performance-monitoring signals are weighted and transformed into an adaptation signal that guides future decisions and actions. The model-based single-trial analysis approach goes far beyond conventional peak-and-trough analyses of event-related potentials and enables testing mechanistic theories of performance monitoring, cognitive control, and decision making.

Combined effects of neuroticism and negative emotional context on spontaneous EEG dynamics.

Neuroticism is a personality trait with great clinical relevance, defined as a tendency to experience negative affect, sustained self-generated negative thoughts and impaired emotion regulation. Here, we investigated spontaneous brain dynamics in the aftermath of negative emotional events and their links with neuroticism in order to shed light on the prolonged activity of large-scale brain networks associated with the control of affect. We recorded electroencephalography (EEG) from 36 participants who were asked to rest after watching neutral or fearful video clips. Four topographic maps (i.e. microstates classes A, B, C and D) explained the majority of the variance in spontaneous EEG. Participants showed greater presence of microstate D and lesser presence of microstate C following exposure to fearful stimuli, pointing to changes in attention- and introspection-related networks previously associated with these microstates. These emotional effects were more pronounced for participants with low neuroticism. Moreover, neuroticism scores were positively correlated with microstate C and negatively correlated with microstate D, regardless of previous emotional stimulation. Our results reveal distinctive effects of emotional context on resting-state EEG, consistent with a prolonged impact of negative affect on the brain, and suggest a possible link with neuroticism.

Modelling EEG Dynamics with Brain Sources

An electroencephalogram (EEG), recorded on the surface of the scalp, serves to characterize the distribution of electric potential during brain activity. This method finds extensive application in investigating brain functioning and diagnosing various diseases. Event-related potential (ERP) is employed to delineate visual, motor, and other activities through cross-trial averages. Despite its utility, interpreting the spatiotemporal dynamics in EEG data poses challenges, as they are inherently subject-specific and highly variable, particularly at the level of individual trials. Conventionally associated with oscillating brain sources, these dynamics raise questions regarding how these oscillations give rise to the observed dynamical regimes on the brain surface. In this study, we propose a model for spatiotemporal dynamics in EEG data using the Poisson equation, with the right-hand side corresponding to the oscillating brain sources. Through our analysis, we identify primary dynamical regimes based on factors such as the number of sources, their frequencies, and phases. Our numerical simulations, conducted in both 2D and 3D, revealed the presence of standing waves, rotating patterns, and symmetric regimes, mirroring observations in EEG data recorded during picture naming experiments. Notably, moving waves, indicative of spatial displacement in the potential distribution, manifested in the vicinity of brain sources, as was evident in both the simulations and experimental data. In summary, our findings support the conclusion that the brain source model aptly describes the spatiotemporal dynamics observed in EEG data.

EEG Dynamics During Treatment of Patients with Polymorphic and Monomorphic Subtypes of Prolonged/Chronic Manic-Delusional States

The aim of the study: was to search for differences in the EEG dynamics during the treatment of patients with polymorphic and monomorphic subtypes of prolonged/chronic manic-delusional states (PMDS). Patients and methods: two groups of female patients aged 18–45 with polymorphic (42 patients) and monomorphic (34 patients) subtypes of PMDS were identifed. In all patients, before and after the course of treatment, a multichannel resting EEG was recorded with the measurement of absolute spectral power in narrow frequency sub-bands. A comparative analysis of quantitative EEG spectral parameters was carried out. Intragroup differences in mean EEG spectral power values before and after treatment were identifed using nonparametric statistical criteria. Methods: clinical-psychopathological, psychometric, neurophysiological, statistical. Results: in the group of patients with polymorphic PMDS, compared to the group of patients with monomorphic PMDS, the EEG slowdown under the influence of treatment was significantly stronger and more generalized (in the form of an increase in the spectral power of the EEG in the delta, theta 1, theta 2 and alpha 1 frequency sub-bands), reflecting a greater strengthening of neurophysiological processes of inhibition, which is associated with an improvement in the clinical condition of patients, especially in relation to manic symptoms. Conclusion: the described differences in the functional organization of brain activity may mediate the features of the clinical picture and therapeutic dynamics of patients with polymorphic and monomorphic subtypes of PMDS.