Patterns Of EEG Activity Research Articles

Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time.

Transcranial magnetic stimulation (TMS) interventions could feasibly treat stroke-related motor impairments, but their effects are highly variable. Brain state-dependent TMS approaches are a promising solution to this problem, but inter-individual variation in lesion location and oscillatory dynamics can make translating them to the poststroke brain challenging. Personalized brain state-dependent approaches specifically designed to address these challenges are therefore needed. As a first step towards this goal, we tested a novel machine learning-based EEG-TMS system that identifies personalized brain activity patterns reflecting strong and weak corticospinal tract (CST) output (strong and weak CST states) in healthy adults in real-time. Participants completed a single-session study that included the acquisition of a TMS-EEG-EMG training dataset, personalized classifier training, and real-time EEG-informed single pulse TMS during classifier-predicted personalized CST states. MEP amplitudes elicited in real-time during personalized strong CST states were significantly larger than those elicited during personalized weak and random CST states. MEP amplitudes elicited in real-time during personalized strong CST states were also significantly less variable than those elicited during personalized weak CST states. Personalized CST states lasted for ~1-2 seconds at a time and ~1 second elapsed between consecutive similar states. Individual participants exhibited unique differences in spectro-spatial EEG patterns between personalized strong and weak CST states. Our results show for the first time that personalized whole-brain EEG activity patterns predict CST activation in real-time in healthy humans. These findings represent a pivotal step towards using personalized brain state-dependent TMS interventions to promote poststroke CST function.

Effects of Exercise Training on EEG Activity Patterns during Cognitive Tasks

To explore the effective method to detect the effect of exercise training on EEG activity, this paper compared the distinguishability of EEG changes relevanted to exercise training in different cognitive states. EEG signals of college students who have undergone long-term exercise training (exercise training group) and college students who have not undergone such training (control group) were collected under specific cognitive tasks (Motor imagination tasks). Then the power characteristics of alpha wave and beta wave were obtained by wavelet transform method. The machine learning method was used to distinguish the difference of brain electrical activity between the exercise-trained subjects and the control group during the exercise-related cognitive tasks. Results showed that EEG patterns between two groups were not distinguished in rest status (the accuracy is lower than 59%), but they were separable in the completion of the motor cognitive tasks. The accuracy was over 85%. The pattern of brain electrical activity during cognitive tasks can better reflect the effect of exercise training.

Visual perspective taking neural processing in forensic cases with high density EEG

This EEG study aims at dissecting the differences in the activation of neural generators between borderline personality disorder patients with court-ordered measures (BDL-COM) and healthy controls in visual perspective taking. We focused on the distinction between mentalizing (Avatar) and non-mentalizing (Arrow) stimuli as well as self versus other-perspective in the dot perspective task (dPT) in a sample of 15 BDL-COM cases and 54 controls, all of male gender. BDL-COM patients showed a late and diffuse right hemisphere involvement of neural generators contrasting with the occipitofrontal topography observed in controls. For Avatars only and compared to controls, the adoption of Self perspective involved a lower EEG activity in the left inferior frontal, right middle temporal cortex and insula in BDL-COM patients prior to 80 ms post-stimulus. When taking the Other-perspective, BDL-COM patients also showed a lower activation of superior frontal, right inferior temporal and fusiform cortex within the same time frame. The beta oscillation power was significantly lower in BDL-COM patients than controls between 400 and 1300 ms post stimulus in the Avatar-Other condition. These results indicate that BDL-COM patients display both altered topography of EEG activation patterns and reduced abilities to mobilize beta oscillations during the treatment of mentalistic stimuli in dPT.

The arousal effect of An-Gong-Niu-Huang-Wan on alcoholic-induced coma rats: A research based on EEG

Ethnopharmacological relevanceAcute alcohol intoxication is one of the leading causes of coma. A well-regarded Chinese herbal formula, known as An-Gong-Niu-Huang-Wan (AGNHW), has garnered recognition for its efficacy in treating various brain disorders associated with impaired consciousness, including acute alcohol-induced coma. Despite its clinical effectiveness, the scientific community lacks comprehensive research on the mechanistic aspects of AGNHW’s impact on the electroencephalogram (EEG) patterns observed during alcohol-induced coma. Gaining a deeper understanding of AGNHW’s mechanism of action in relation to EEG characteristics would hold immense importance, serving as a solid foundation for further advancing its clinical therapeutic application. Aim of the studyThe study sought to investigate the impact of AGNHW on EEG activity and sleep EEG patterns in rats with alcoholic-induced coma. Materials and methodsA rat model of alcohol-induced coma was used to examine the effects of AGNHW on EEG patterns. Male Sprague-Dawley rats were intraperitoneally injected with 32% ethanol to induce a coma, followed by treatment with AGNHW. Wireless electrodes were implanted in the cortex of the rats to obtain EEG signals. Our analysis focused on evaluating alterations in the Rat Coma Scale (RCS), as well as assessing changes in the frequency and distribution of EEG patterns, sleep rhythms, and body temperature subsequent to AGNHW treatment. ResultsThe study found a significant increase in the δ-band power ratio, as well as a decrease in RCS scores and β-band power ratio after modeling. AGNHW treatment significantly reduced the δ-band power ratio and increased the β-band power ratio compared to naloxone, suggesting its superior arousal effects. The results also revealed a decrease in the time proportion of WAKE and REM EEG patterns after modeling, accompanied by a significant increase in the time proportion of NREM EEG patterns. Both naloxone and AGNHW effectively counteracted the disordered sleep EEG patterns. Additionally, AGNHW was more effective than naloxone in improving hypothermia caused by acute alcohol poisoning in rats. ConclusionOur study provides evidence for the arousal effects of AGNHW in alcohol-induced coma rats. It also suggests a potential role for AGNHW in regulating post-comatose sleep rhythm disorders.

Disrupted sleep-wake regulation in the MCI-Park mouse model of Parkinson’s disease

Disrupted sleep has a profound adverse impact on lives of Parkinson’s disease (PD) patients and their caregivers. Sleep disturbances are exceedingly common in PD, with substantial heterogeneity in type, timing, and severity. Among the most common sleep-related symptoms reported by PD patients are insomnia, excessive daytime sleepiness, and sleep fragmentation, characterized by interruptions and decreased continuity of sleep. Alterations in brain wave activity, as measured on the electroencephalogram (EEG), also occur in PD, with changes in the pattern and relative contributions of different frequency bands of the EEG spectrum to overall EEG activity in different vigilance states consistently observed. The mechanisms underlying these PD-associated sleep-wake abnormalities are poorly understood, and they are ineffectively treated by conventional PD therapies. To help fill this gap in knowledge, a new progressive model of PD – the MCI-Park mouse – was studied. Near the transition to the parkinsonian state, these mice exhibited significantly altered sleep-wake regulation, including increased wakefulness, decreased non-rapid eye movement (NREM) sleep, increased sleep fragmentation, reduced rapid eye movement (REM) sleep, and altered EEG activity patterns. These sleep-wake abnormalities resemble those identified in PD patients. Thus, this model may help elucidate the circuit mechanisms underlying sleep disruption in PD and identify targets for novel therapeutic approaches.

Intracranial recordings reveal high-frequency activity in the human temporal-parietal cortex supporting non-literal language processing.

Non-literal expressions such as sarcasm, metaphor and simile refer to words and sentences that convey meanings or intentions that are different and more abstract than literal expressions. Neuroimaging studies have shown activations in a variety of frontal, parietal and temporal brain regions implicated in non-literal language processing. However, neurophysiological correlates of these brain areas underlying non-literal processing remain underexplored. To address this, we investigated patterns of intracranial EEG activity during non-literal processing by leveraging a unique patient population. Seven neurosurgical patients with invasive electrophysiological monitoring of superficial brain activity were recruited. Intracranial neural responses were recorded over the temporal-parietal junction (TPJ) and its surrounding areas while patients performed a language task. Participants listened to vignettes that ended with non-literal or literal statements and were then asked related questions to which they responded verbally. We found differential neurophysiological activity during the processing of non-literal statements as compared to literal statements, especially in low-Gamma (30-70 Hz) and delta (1-4 Hz) bands. In addition, we found that neural responses related to non-literal processing in the high-gamma band (>70 Hz) were significantly more prominent at TPJ electrodes as compared to non-TPJ (i.e., control) electrodes in most subjects. Moreover, in half of patients, high-gamma activity related to non-literal processing was accompanied by delta-band modulation. These results suggest that both low- and high-frequency electrophysiological activities in the temporal-parietal junction play a crucial role during non-literal language processing in the human brain. The current investigation, utilizing better spatial and temporal resolution of human intracranial electrocorticography, provides a unique opportunity to gain insights into the localized brain dynamics of the TPJ during the processing of non-literal language expressions.

Brain signatures indexing variation in internal processing during perceptual decision-making

Brain activity is highly variable during a task. Discovering, characterizing, and linking variability in brain activity to internal processes has primarily relied on experimental manipulations. However, changes in internal processing could arise from many factors independent of experimental conditions. Here we utilize a data-driven clustering method based on modularity-maximation to identify consistent spatial-temporal EEG activity patterns across individual trials. Subjects (N= 25) performed a motion discrimination task with six interleaved levels of coherence. Clustering identified two discrete subtypes of trials with different patterns of activity. Surprisingly, Subtype 1 occurred more frequently in trials with lower motion coherence but was associated with faster response times. Computational modeling suggests that Subtype 1 was characterized by a lower threshold for reaching a decision. These results highlight across-trial variability in decision processes traditionally hidden to experimenters and provide a method for identifying endogenous brain state variability relevant to cognition and behavior.

Deep learning on independent spatial EEG activity patterns delineates time windows relevant for response inhibition.

Inhibitory control processes are an important aspect of executive functions and goal-directed behavior. However, the mostly correlative nature of neurophysiological studies was not able to provide insights which aspects of neural dynamics can best predict whether an individual is confronted with a situation requiring the inhibition of a response. This is particularly the case when considering the complex spatio-temporal nature of neural processes captured by EEG data. In the current study, we ask whether independent spatial activity profiles in the EEG data are useful to predict whether an individual is confronted with a situation requiring response inhibition. We combine independent component analysis (ICA) with explainable artificial intelligence approaches (EEG-based deep learning) using data from a Go/Nogo task (N = 255 participants). We show that there are four dissociable spatial activity profiles important to classify Go and Nogo trials as revealed by deep learning. Of note, for all of these four independent activity profiles, neural activity in the time period between 300 and 550 ms after stimulus presentation was most informative. Source localization analyses further revealed regions in the pre-central gyrus (BA6), the middle frontal gyrus (BA10), the inferior frontal gyrus (BA46), and the insular cortex (BA13) were associated with the isolated spatial activity profiles. The data suggest concomitant processes being reflected in the identified time window. This has implications for the ongoing debate on the functional significance of event-related potential correlates of inhibitory control.

Higher levels of narrativity lead to similar patterns of posterior EEG activity across individuals.

The focus of cognitive and psychological approaches to narrative has not so much been on the elucidation of important aspects of narrative, but rather on using narratives as tools for the investigation of higher order cognitive processes elicited by narratives (e.g., understanding, empathy, etc.). In this study, we work toward a scalar model of narrativity, which can provide testable criteria for selecting and classifying communication forms in their level of narrativity. We investigated whether being exposed to videos with different levels of narrativity modulates shared neural responses, measured by inter-subject correlation, and engagement levels. Thirty-two participants watched video advertisements with high-level and low-level of narrativity while their neural responses were measured through electroencephalogram. Additionally, participants' engagement levels were calculated based on the composite of their self-reported attention and immersion scores. Results demonstrated that both calculated inter-subject correlation and engagement scores for high-level video ads were significantly higher than those for low-level, suggesting that narrativity levels modulate inter-subject correlation and engagement. We believe that these findings are a step toward the elucidation of the viewers' way of processing and understanding a given communication artifact as a function of the narrative qualities expressed by the level of narrativity.

Motor-effector dependent modulation of sensory-motor processes identified by the multivariate pattern analysis of EEG activity

Sensory information received through sensory organs is constantly modulated by numerous non-sensory factors. Recent studies have demonstrated that the state of action can modulate sensory representations in cortical areas. Similarly, sensory information can be modulated by the type of action used to report perception; however, systematic investigation of this issue is scarce. In this study, we examined whether sensorimotor processes represented in electroencephalography (EEG) activities vary depending on the type of effector behavior. Nineteen participants performed motion direction discrimination tasks in which visual inputs were the same, and only the effector behaviors for reporting perceived motion directions were different (smooth pursuit, saccadic eye movement, or button press). We used multivariate pattern analysis to compare the EEG activities for identical sensory inputs under different effector behaviors. The EEG activity patterns for the identical sensory stimulus before any motor action varied across the effector behavior conditions, and the choice of motor effectors modulated the neural direction discrimination differently. We suggest that the motor-effector dependent modulation of EEG direction discrimination might be caused by effector-specific motor planning or preparation signals because it did not have functional relevance to behavioral direction discriminability.

Reconstructing attended and unattended colors from human scalp electroencephalography

Human electroencephalography (EEG) studies have shown that attention enhances relevant over irrelevant visual features (e.g., Andersen et al., 2013). Such amplitude modulations have been a major focus of feature-based attention research, alongside recent studies showing that the spatial pattern of scalp-recorded EEG activity conveys critical information about different stimuli. To date, the majority of these studies have focused on decoding visual-spatial features (location, orientation). Here, we test whether color—a non-spatial feature—can be reconstructed from the scalp activity pattern using steady-state visual evoked potentials (SSVEPs) and inverted encoding modeling (IEM). Furthermore, we test how these color reconstructions vary across different types of feature-based attention tasks. Participants (N=17) performed two color-based attention tasks: 1) a non-competitive task, in which they monitored a single array of colored dots to detect a brief interval of coherent motion; 2) a selective attention task in which participants focused on target-colored dots among distractors to detect a brief luminance decrease. In the selective task, the target and distractor colors were either distinct (180° away on a CIELab colorwheel) or similar (63° away). Stimuli flickered at different frequencies to elicit separable SSVEPs that we used to generate color-selective response profiles based on their spatially distributed patterns. Across both tasks, we found that information about the target and distractor colors was reliably recovered from single trials with IEMs, confirming the usefulness of this technique for investigating feature representations. Model-based reconstructions in the selective attention task were stronger for more distinct colors, suggesting that perceptually similar features might elicit less informative SSVEP signals due to greater overlap in the neural populations that encode them. Broadly, our results demonstrate that SSVEPs along with IEMs can be used to investigate non-spatial visual features that may not be as well represented as spatial information in event-related potentials or alpha-band signals.

Capacity of Autonomous Sensory Meridian Response on the Reduction of Mental Stress.

In a social environment, various types of stress can be overwhelming. Humans frequently encounter these stressful situations in social life. Stress is divided into physical stress and mental stress; the latter is induced by heavy mental workloads and has become a huge social problem, leading to mental disorders and possibly suicide in the worst scenario. Investigations into monitoring mental stress and reducing stressful conditions are, therefore, important for its prevention. In the present study, we focused on autonomous sensory meridian response (ASMR) sound, which is known to improve the human mental condition through its comforting and relaxing effects. We investigated the effect of ASMR on the mental workload induced by mental tasks by the evaluation of EEG activation patterns in normal subjects. Our results showed a significant decrease in alpha-band activity and a significant increase in gamma (high beta)-band activity under the induction of mental workload by mental tasks compared to the resting condition. When applying ASMR sound, alpha- and gamma-band activity under the induction of mental workload by mental tasks was restored to the level of the resting condition. In conclusion, these results indicate that ASMR sound reduces the mental stress induced by mental workload.

An exploratory study of drivers’ EEG response during emergent collision avoidance

Introduction: EEG (electroencephalogram) has been applied as a valuable measure to estimate drivers’ mental status and cognitive workload during driving tasks. However, most previous studies have focused on the EEG features at particular driver status, such as fatigue or distraction, with less attention paid to EEG response in emergent and safety–critical situations. This study aims to investigate the underlying patterns of different EEG components during an emergent collision avoidance process. Method: A driving simulator experiment was conducted with 38 participants (19 females and 19 males). The scenario included a roadside pedestrian who suddenly crossed the road when the driver approached. The participants’ EEG data were collected during the pedestrian-collision avoidance process. The log-transformed power and power ratio of four typical EEG components (i.e., delta, theta, alpha and beta) were extracted from four collision avoidance stages: Stage 1-normal driving stage, Stage 2-hazard perception stage, Stage 3-evasive action stage, and Stage 4-post-hazard stage. Results: The activities of all four EEG bands changed consistently during the collision avoidance process, with the power increased significantly from Stage 1 to Stage 4. Drivers who collided with the pedestrian and drivers who avoided the collision successfully did not show a significant difference in EEG activity across the stages. Male drivers had a higher delta power ratio and lower alpha power ratio than females in both hazard perception and evasive action stages. Conclusions: Enhanced activities of different EEG bands could be concurrent at emergent and safety–critical situations. Female drivers were more mentally aroused than male drivers during the collision avoidance process. Practical Applications: The study generates more understanding of drivers’ neurophysiological response in an emergent and safety–critical collision avoidance event. Driver state monitoring and warning systems that aim to assist drivers in impending collisions may utilize the patterns of EEG activity identified in the collision avoidance process.

The presence of occlusion affects electroencephalogram activity patterns when the target is occluded and immediately before occlusion

The objective of this study was to clarify the differences in electroencephalogram (EEG) activity patterns during the eye movements required to trace visible and occluded moving targets with millisecond temporal resolution. To achieve this objective, we simultaneously measured EEG and eye-tracking during a task that required tracking moving targets that were partially occluded. These EEG and eye tracker parameters were compared with those of a nonoccluded task, a control task in which the moving target was fully visible. Differences in EEG activity patterns in the parietal eye field and posterior parietal lobe were observed during the occluded sections in the occluded task compared with the nonoccluded task. Under the same conditions, differences in EEG activity patterns in the middle temporal visual area, posterior parietal lobe, premotor, primary motor cortex, and temporal lobe were observed during visible sections in the occluded task compared with the nonoccluded task. These results indicate that the presence of occlusion affects EEG activity patterns not only when the target is occluded but also immediately before occlusion.

Multivariate prediction of pain perception based on pre-stimulus activity

The perception of pain is modulated by different processes such as, for example, expectations and attention regarding the upcoming stimulus. Such processes are initiated prior to the actual stimulus and are reflected in ongoing brain activity. Different processes that are by definition also complex in itself are reflected in pre-stimulus activity and therefore the detection of this activity pattern should benefit from a multivariate approach. To identify specific pre-stimulus EEG activity patterns related to subsequent pain perception in humans, we contrasted painful with non-painful sensations delivered at the individual threshold level during EEG measurements. The results of the multivariate EEG analysis revealed a high level of accuracy (group mean 68%) in predicting the pain categorization solely based on pre-stimulus activity. In particular, fronto-central regions and activity in the higher gamma band (60:120 Hz) were of maximal importance for classification. Additional analyses supported the specific role of the pattern of high gamma band activity prior to the stimulus for predicting the behavioral outcome and demonstrated that the informational value embedded in the pre-stimulus activity is nearly as informative as the post-stimulus processing and reflects a specific preparatory state. Further, a close relation between pre- and post-stimulus processing in the high gamma band was observed. These findings support the important role of a multivariate cognitive state prior to stimulus appearance for the emergence of the subjective perception of pain and the functional role of widespread high gamma band activity.

Move your virtual body: differences and similarities in brain activation patterns during hand movements in real world and virtual reality

Virtual reality (VR) is a promising tool for neurological rehabilitation, especially for motor rehabilitation. In the present study, we investigate whether brain activation patterns that are evoked by active movements are comparable when these movements are carried out in reality and in VR. Therefore, 40 healthy adults (20 men, mean age 25.31 years) performed hand movements and viewed these movements in a first-person view in reality, a VR scene showing realistic virtual hands, and a VR scene showing abstract virtual hands, in a randomized order. The VR conditions were presented via an immersive 3D head-mounted display system. EEG activity was assessed over the hand motor areas during and after movement execution. All three conditions led to typical EEG activation patterns over the motor cortex. Hence, brain activation patterns were largely comparable between conditions. However, the VR conditions, especially the abstract VR condition, led to a weaker hemispheric lateralization effect compared to the real-world condition. This indicates that hand models in VR should be realistic to be able to evoke activation patterns in the motor cortex comparable to real-world scenarios.

Linear-Threshold Dynamics for the Study of Epileptic Events

In this letter, we provide a detailed characterization of the equilibria and bifurcations of two-dimensional linear-threshold models. Using the input to the system as the bifurcation parameter, we characterize the location of the admissible equilibria, show that bifurcations can arise only when equilibria lie on the boundary of well-defined regions of the state space, and prove that (codimension-one) bifurcations can only be of three different types: persistent, non-smooth fold, and Hopf. We show how these bifurcations change the qualitative properties of the system trajectories, and how these behaviors resemble prototypical patterns of EEG activity observed before, during, and after seizure events in the human brain. Our findings suggest that low-dimensional linear threshold models can effectively be used to model, analyze, predict, and ultimately regulate the interactions of neuronal populations in the human brain.

Separability of Motor Imagery Directions Using Subject-Specific Discriminative EEG Features

Electroencephalogram (EEG) based brain–computer interface (BCI) is an augmented communication modality between the brain and computer that exclusively depends on noninvasively recorded neuro-electrical activity. To establish multiple control commands in BCI systems, it is essential to efficiently identify unique EEG activation patterns underlying multiple motor tasks. This article investigates the brain activation modulations elicited by imagined movement directions and the EEG features that characterize them. An experiment was conducted in which ten subjects imagined two-dimensional right-hand movement in the left, right, up, and down directions in the vertical plane. The frequency and phase characteristics of EEG rhythms are explored to identify the features that discriminate movement imagination toward multiple directions. The proposed decoding step consists of Fisher's analysis of instantaneous phase values to identify EEG channels with optimal discriminative information followed by extraction of phase-locking values and temporal-spatial features, which are then used for classification. The proposed approach offers significantly higher ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.05) performance than the state-of-the-art method while employing subject-specific time segments and channels. Hence, an average accuracy of 69.25% is obtained for binary classification of all pairs of movement directions with a performance increment of 8.33% compared to the conventional approaches using fixed channel-time paradigm. The results illustrate the potential use of noninvasive EEG signals in decoding imagined movement kinematics, and further research is needed to investigate and optimize the separability in multiple directions.

Mental individuation of imagined finger movements can be achieved using TMS-based neurofeedback

Neurofeedback (NF) in combination with motor imagery (MI) can be used for training individuals to volitionally modulate sensorimotor activity without producing overt movements. However, until now, NF methods were of limited utility for mentally training specific hand and finger actions. Here we employed a novel transcranial magnetic stimulation (TMS) based protocol to probe and detect MI-induced motor activity patterns in the primary motor cortex (M1) with the aim to reinforce selective facilitation of single finger representations. We showed that TMS-NF training but not MI training with uninformative feedback enabled participants to selectively upregulate corticomotor excitability of one finger, while simultaneously downregulating excitability of other finger representations within the same hand. Successful finger individuation during MI was accompanied by strong desynchronization of sensorimotor brain rhythms, particularly in the beta band, as measured by electroencephalography. Additionally, informative TMS-NF promoted more dissociable EEG activation patterns underlying single finger MI, when compared to MI of the control group where no such feedback was provided. Our findings suggest that selective TMS-NF is a new approach for acquiring the ability of finger individuation even if no overt movements are performed. This might offer new treatment modality for rehabilitation after stroke or spinal cord injury.

Representational Pattern Similarity of Electrical Brain Activity Reveals Rapid and Specific Prediction during Language Comprehension.

Predicting upcoming events is a critical function of the brain, and language provides a fertile testing ground for studying prediction, as comprehenders use context to predict features of upcoming words. Many aspects of the mechanisms of prediction remain elusive, partly due to a lack of methodological tools to probe prediction formation in the moment. To elucidate what features are neurally preactivated and when, we used representational similarity analysis on previously collected sentence reading data. We compared EEG activity patterns elicited by expected and unexpected sentence final words to patterns from the preceding words of the sentence, in both strongly and weakly constraining sentences. Pattern similarity with the final word was increased in an early time window following the presentation of the pre-final word, and this increase was modulated by both expectancy and constraint. This was not seen at earlier words, suggesting that predictions were precisely timed. Additionally, pre-final word activity-the predicted representation-had negative similarity with later final word activity, but only for strongly expected words. These findings shed light on the mechanisms of prediction in the brain: rapid preactivation occurs following certain cues, but the predicted features may receive reduced processing upon confirmation.