Patterns Of Brain Electrical Activity Research Articles

Brain electrical patterns associated with pleasure and emotion induced by tonal and atonal music.

Several studies in the last 40 years have used electroencephalography (EEG) to recognize patterns of brain electrical activity correlated with emotions evoked by various stimuli. For example, the frontal alpha and theta asymmetry models to distinguish musical emotions and musical pleasure, respectively. Since these studies have used mainly tonal music, in this study, we decided to incorporate both tonal (n = 8) and atonal (n = 8) musical stimuli to observe the subjective and electrophysiological responses associated with valence, arousal, pleasure, and familiarity, from 25 nonmusician Mexican adults (10 females, 15 males; M = 37.8 years old, SD = 15.1). Our results showed that atonal music was perceived as less familiar and pleasurable than tonal music, according to the average subjective ratings. Interestingly, greater right hemispheric activity (alpha suppression) was associated with atonal music listening. Additionally, there was an increase of theta power at the right frontal cortex (F4) correlated with a decrease of pleasure ratings, in line with the frontal theta asymmetry (FTA) model. Finally, according to the model of frontal alpha asymmetry (FAA) to distinguish musical emotions, activation (alpha suppression) of the left frontal cortex (F3) was correlated with greater valence and arousal-that is, joyful music. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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.

Hippocampal network activity forecasts epileptic seizures.

Seizures in people with epilepsy were long thought to occur at random, but recent methods for seizure forecasting enable estimation of the likelihood of seizure occurrence over short horizons. These methods rely on days-long cyclical patterns of brain electrical activity and other physiological variables that determine seizure likelihood and that require measurement through long-term, multimodal recordings. In this retrospective cohort study of 15 adults with bitemporal epilepsy who had a device that provides chronic intracranial recordings, functional connectivity of hippocampal networks fluctuated in multiday cycles with patterns that mirrored cycles of seizure likelihood. A functional connectivity biomarker of seizure likelihood derived from 90-s recordings of background hippocampal activity generalized across individuals and forecasted 24-h seizure likelihood as accurately as cycle-based models requiring months-long baseline recordings. Larger, prospective studies are needed to validate this approach, but our results have the potential to make reliable seizure forecasts accessible to more people with epilepsy.

Temporal Coherence in the Synchronization of Brain Electrical Activity Patterns: An Application with the RMS Fluctuation Function

In this article, we performed a preliminary test with the mean square fluctuation (RMS) function, using EEG data, in which the task consisted of the response given to the real/imagined motor stimulus. All steps, up to the configuration of the RMS function, relied on information from subject S010, available in the Physionet database. We analyzed 12 of the 64 channels and four stimuli. We evaluated the amplitude of channels 32 and 37 in relation to the others (11, 22, 24, 43, 44, 49, 54, 61, 63 and 64). We quantitatively observed similarities when the brain perfoRMS the same real/imagined motor task, and that the timing of the amplitude changes with increasing n scale (time scales). In all experiments (S010R03, S010I03, S010R04, S010I04, S010R05, S010I05, S010R06 and S010I06), channels 32 x 22(∆logF32,22) and 37 x 24 (∆logF37,24), for n > 100 (60 seconds), were smaller than the others, showing so channels 32 and 37 (left/right hemisphere) have the greatest fluctuation and differences between them. From data processing (.EDF) to visualization of the curves (FDFA / ∆log), we concluded that it is possible to replicate the test with more channels and subjects, as well as investigate other types of activities of the human brain with signals extracted from the EEG.

Placing willed attention in context: a review of attention and free will

Attention is the ability to focus one's awareness on relevant events and objects while ignoring distracting ones. Laboratory studies of top-down voluntary attention commonly use predictive or instructional cues to direct attention. However, in real world scenarios, voluntary attention is not necessarily externally cued, but may be focused by internal, self-generated processes. The voluntary focusing of attention in the absence of external guidance has been referred to as “willed attention,” a term borrowed from the literature on willed motor actions. In a fashion similar to studies of willed (self-initiated) actions, during willed attention, participants are given the freedom to deploy attention based on their own free choices. Electrophysiological studies have shown that during willed attention, ongoing neural activity biases willed attention decisions on a moment-to-moment basis as reflected in transient patterns of brain electrical activity that predict where participants will later choose to focus their attention. Brain imaging studies have revealed that compared to cued attention, willed attention involves additional frontal cortical structures, which interact with the classic attentional control networks of the human brain to produce a modified network organization for willed attention control. In this introduction to willed attention, we briefly review the fields of voluntary attention and self-initiated motor actions, in order to describe willed attention and its neural correlates as they relate to the broader concepts of attention and volition.

Cognitive functions and patterns of brain activity in patients after simultaneous coronary and carotid artery revascularization.

On-pump coronary artery bypass grafting (CABG) is associated with a high risk of neurological complications in patients with severe carotid stenosis. Moreover, early postoperative cognitive dysfunction (POCD) incidence remains high in patients undergoing simultaneous coronary and carotid surgery. Recent studies have shown that even moderate carotid stenosis (≥50%) is associated with postoperative cognitive decline after CABG. Data on brain health in the postoperative period of simultaneous coronary and carotid surgery are limited. This study aimed to analyze early postoperative changes in the cognitive function and patterns of brain electrical activity in patients after simultaneous coronary and carotid artery revascularization. Between January 2017 and December 2020, consecutive patients were assigned to on-pump CABG with or without carotid endarterectomy (CEA) according to clinical indications. An extended neuropsychological and electroencephalographic (EEG) assessment was performed before surgery and at 7-10 days after CABG or CABG + CEA. A total of 100 patients were included [median age 59 (55; 65), 95% men, MMSE 27 (26; 28)], and among these, 46 underwent CEA. POCD was diagnosed in 29 (63.0%) patients with CABG + CEA and in 32 (59.0%) patients with isolated CABG. All patients presented with a postoperative theta power increase. However, patients with CABG + right-sided CEA demonstrated the most pronounced theta power increase compared to patients with isolated CABG. The findings of our study show that patients with CABG + CEA and isolated CABG have comparable POCD incidence; however, patients with CABG + right-sided CEA presented with lower brain activity.

Dynamics of Brain Electrical Activity Patterns in Maladaptive Disorders

Dynamics of Brain Electrical Activity Patterns in Maladaptive Disorders

Evaluating the cognitive and psychological effects of real-time auditory travel information on drivers using EEG

ABSTRACT Real-time travel information design with inadequate consideration of human factors can lead to driver distraction and diminish road safety. This study measures drivers’ brain electrical activity patterns to evaluate multiple aspects of driver cognition and psychology under real-time information provision using insights from the neuroscience domain on the localisation of brain functions. The brain electrical activity patterns of 84 participants are collected using an electroencephalogram (EEG) in an interactive driving simulator environment. The impacts of real-time auditory travel information characteristics (amount, sufficiency, and content) and different time stages of interaction with information provision (before, during, and after) on the frequency band powers of EEG signals in different brain regions are analyzed using linear mixed models. Study results illustrate that drivers exert more cognitive effort to perceive/process information on routes with complex driving environments. Insufficient information may evoke increased attention to internal processing and memory processing on routes characterised by higher travel time uncertainty, while route recommendation to switch to such routes may increase drivers’ stress and anxiety. The study findings can aid information providers, both private and public, as well as auto manufacturers to incorporate driver cognition in designing safer real-time information and their delivery systems.

Hybrid route choice model incorporating latent cognitive effects of real-time travel information using physiological data

The proliferation of information systems is enabling drivers to receive en route real-time travel information, often from multiple sources, for making informed routing decisions. A robust understanding of route choice behavior under information provision can be leveraged by traffic operators to design information and its delivery systems for managing network-wide traffic. However, most existing route choice models lack the ability to consider the latent cognitive effects of information on drivers and their implications on route choice decisions. This paper presents a hybrid route choice modeling framework that incorporates the latent cognitive effects of real-time information and the effects of several explanatory variables that can be measured directly (i.e., route characteristics, information characteristics, driver attributes, and situational factors). The latent cognitive effects are estimated by analyzing drivers’ physiological data (i.e., brain electrical activity patterns) measured using an electroencephalogram (EEG). Data was collected for 95 participants in driving simulator experiments designed to elicit realistic route choices using a network-level setup featuring routes with different characteristics (in terms of travel time and driving environment complexity) and dynamic ambient traffic. Averaged EEG band powers in multiple brain regions were used to extract two latent cognitive variables that capture driver’s cognitive effort during and immediately after the information provision, and cognitive inattention before implementing the route choice decision. A Multiple Indicators Multiple Causes model was used to test the effects of several explanatory factors on the latent cognitive variables, and their combined impacts on route choice decisions. The study results highlight the significant effects of driver attributes and information characteristics on latent cognitive effort and of route characteristics on latent cognitive inattention. They also indicate that drivers who are more attentive and exert more cognitive effort are more likely to switch from their current route by complying with the information provided. The study insights can aid traffic operators and information service providers to incorporate human factors and cognitive aspects while devising strategies for designing and disseminating real-time travel information to influence drivers’ route choices.

Classifying electrical activity of the brain during imaginary movements of untrained subjects using artificial neural networks

Introduction: Developing new classification methods for human brain electrical activity patterns corresponding to actual movements or motor imagery is an essential interdisciplinary problem in brain-computer interface research. One of the most promising approaches is the development of methods based on artificial neural networks. Purpose: The development of ANN-based methods for classifying electroencephalographic patterns associated with motor imagery in untrained subjects. Methods: Classifiers based on linear neural networks, multi-layer perceptrons, radial basis function networks and support vector machines. Results: The authors selected the optimal type, topology, learning algorithms and parameters of an artificial neural network in order to provide the most accurate and fast classification of lower limb motor imagery EEG signals. It has been studied how the number of the analyzed channels of a multichannel EEG and their choice affect the quality of motor imagery patterns classification. Optimal configurations were obtained for the electrode arrangements. The influence of EEG pre-processing on the accuracy of motor imagery recognition was analyzed. A computational experiment showed the accuracy of 90-95% in untrained subjects. Radial basis function network demonstrated the best performance. Besides, the dataset dimensionality has been significantly reduced down to 6–12 channels without any classification accuracy loss. Practical relevance: The obtained results can be useful for the developers of motor imagery EEG classification algorithms used in brain-computer interfaces.

P16-F Generation of burst-suppression pattern on electroencephalography and electrocorticography in general anesthesia: Dose–effect relationship

Background Electroencephalographic (EEG) patterns of brain electrical activity (BEA) reflect the level of anesthesia during neurosurgery. Excessive levels of anesthesia are accompanied by burst-suppression pattern (BSP) or even electrical inactivity. Moderate anesthesia is necessary to ensure optimal results of intraoperative motor mapping. We aimed to determine median effective doses (ED50) for propofol or sevoflurane anesthesia that are associated with BSP in EEG and electrocorticographic (ECoG) recordings. Material and methods Data of 60 patients (m/f = 32/28, age 21–65, BMI Results ED50 of anesthetics for EEG were greater than those for ECoG for both drugs. ED50 for propofol equaled 4.6 ± 0.4 mg/kg/h for ECoG and 6.9 ± 0.9 mg/kg/h for EEG. Median MAC of sevoflurane was 0.51 ± 0.16 for ECoG and 1.08 ± 0.18 for EEG. Conclusions EEG-based techniques for anesthesia level assessment may be less sensitive than ECoG-based.

Suppression of brain electrical activity in general anesthesia: the dose-effect relationship

The purpose of this study was to determine to mean effective dosage for propofol and sevoflurane that leads to the generation of burst-suppression pattern on electroencephalogram and electrocorticogram in general anesthesia. It is established that effective dosages that cause the generation of intermittent activity on electroencephalogram exceed those for electrocorticogram. This trend is common both for sevoflurane and for propofol. Discrepancies in the dosage for general anesthetics causing the suppression of electrical activity on electroencephalogram and electrocorticogram defined the «dissociation» of recorded patterns. Simultaneous recording of electroencephalogram and electrocorticogram showed cortical burst-suppression pattern together with the continuous pattern on scalp electrodes. It is concluded that rating and systematization for suppression patterns on electroencephalogram and electrocorticogram should be built upon the common foundation, but with allowances made for technical specifics. Categorization of suppression phenomenon on electrocorticogram implies the critical amplitude to be 20 mkV, whereas for electroencephalogram it is 10 mkV. More formal proposed methods for assessment of intermittent patterns of brain electrical activity during general anesthesia are based on the specific tasks for intraoperative neuromonitoring. Excessive levels of sedation causing rough depression of electrical activity should be avoided, since they may lead to false-negative results of the neuromonitoring.

THE ROLE OF NEUROMARKERS IN THE DIAGNOSIS OF ADHD

The article presents an overview of classic and modern research concerning the role of neuromarkers in the diagnosis of attention-deficit/hyperactivity disorder (ADHD) and biological correlates of this disorder associated with the dysfunction specific brain structures. ADHD is the most difficult area to diagnosing neurodevelopmental disorders due to the lack of permanent diagnostic criteria of this disorder and comorbidities, and also a large diversity of symptoms. Some of the criteria for this disorder such as age also have little diagnostic value and require a new discussion. In the circumstances of difficulties in diagnosis may help study of neuromarkers of ADHD, derived by analyzing the patterns of brain electrical activity in the EEG. Thus diagnosis may become easier, faster, and most importantly, more accurate.

Functional Equivalence of Imagined vs. Real Performance of an Inhibitory Task: An EEG/ERP Study.

Early neuroimaging and electrophysiological studies suggested that motor imagery recruited a different network than motor execution. However, several studies have provided evidence for the involvement of the same circuits in motor imagery tasks, in the absence of overt responses. The present study aimed to test whether imagined performance of a stop-signal task produces a similar pattern of motor-related EEG activity than that observed during real performance. To this end, mu and beta event-related desynchronization (ERD) and the Lateralized Readiness Potential (LRP) were analyzed. The study also aimed to clarify the functional significance of the Stop-N2 and Stop-P3 event-related potential (ERPs) components, which were also obtained during both real and imagined performance. The results showed a common pattern of brain electrical activity, and with a similar time course, during covert performance and overt execution of the stop-signal task: presence of LRP and Stop-P3 in the imagined condition and identical LRP onset, and similar mu and beta ERD temporal windows for both conditions. These findings suggest that a similar inhibitory network may be activated during both overt and covert execution of the task. Therefore, motor imagery may be useful to improve inhibitory skills and to develop new communicating systems for Brain-Computer Interface (BCI) devices based on inhibitory signals.

Mechanisms underlying brain monitoring during anesthesia: limitations, possible improvements, and perspectives.

Currently, anesthesiologists use clinical parameters to directly measure the depth of anesthesia (DoA). This clinical standard of monitoring is often combined with brain monitoring for better assessment of the hypnotic component of anesthesia. Brain monitoring devices provide indices allowing for an immediate assessment of the impact of anesthetics on consciousness. However, questions remain regarding the mechanisms underpinning these indices of hypnosis. By briefly describing current knowledge of the brain's electrical activity during general anesthesia, as well as the operating principles of DoA monitors, the aim of this work is to simplify our understanding of the mathematical processes that allow for translation of complex patterns of brain electrical activity into dimensionless indices. This is a challenging task because mathematical concepts appear remote from clinical practice. Moreover, most DoA algorithms are proprietary algorithms and the difficulty of exploring the inner workings of mathematical models represents an obstacle to accurate simplification. The limitations of current DoA monitors — and the possibility for improvement — as well as perspectives on brain monitoring derived from recent research on corticocortical connectivity and communication are also discussed.

Relations between infants' emerging reach-grasp competence and event-related desynchronization in EEG.

Recent reports of similar patterns of brain electrical activity (electroencephalogram: EEG) during action execution and observation, recorded from scalp locations over motor-related regions in infants and adults, have raised the possibility that two foundational abilities--controlling one's own intentional actions and perceiving others' actions--may be integrally related during ontogeny. However, to our knowledge, there are no published reports of the relations between developments in motor skill (i.e. recording actual motor skill performance) and EEG during both action execution and action observation. In the present study we collected EEG from 21 9-month-olds who were given opportunities to reach for toys and who also observed an experimenter reach for toys. Event-related desynchronization (ERD) was computed from the EEG during the reaching events. We assessed infants' reaching-grasping competence, including reach latency, errors, preshaping of the hand, and bimanual reaches, and found that desynchronization recorded in scalp electrodes over motor-related regions during action observation was associated with action competence during execution. Infants who were more competent reachers, compared to less competent reachers, exhibited greater ERD while observing reaching-grasping. These results provide initial evidence for an early emerging neural system integrating one's own actions with the perception of others' actions.

Pattern discrimination of neuroelectric waveforms evoked by synchronized visuo-motor integration.

Today, there is a significant demand for fast, accurate, and automated methods for the discrimination of latent patterns in neuroelectric waveforms. One of the main challenges is the development of efficient feature extraction tools to utilize the rich spatio-temporal information inherent in large scale human electrocortical activity. In this paper, our aim is to isolate the most suitable feature extraction method for accurate classification of EEG data related to distinct modes of sensorimotor integration. Our results demonstrate that with some user-dependent input for feature space constraint, a simple classification framework can be developed to accurately distinguish between brain electrical activity patterns during two distinct conditions.

Classification of Mental States Based on Spatiospectral Patterns of Brain Electrical Activity

Abstract: Classification of human thought is an emerging research field that may allow us to understand human brainfunctions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introducea new approach to classify various mental states from noninvasive electrophysiological recordings of human brainactivity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signalsrecorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spa-tiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-relatedsynchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current(input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices.Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For theverification of our approach, eight participants took part in the present study; their EEG data were recorded whilethey performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequentlybetween trials in the same class than th ose in different classes, indicating that th ese spatiospectral pattern maps couldbe used to classify different mental states. The classification accuracy was evaluated for each participant from boththe proposed approach and a conventional mental state classification method based on the inter-hemispheric spectralpower asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (± 9.64%) wasattained for the proposed method; whereas an average accuracy of 57% (± 5.68%) was attained for the conventionalmethod (significance was assessed by the one-tail paired t-test, p < 0.01), showing that the proposed simple clas-sification approach might be one of the promising methods in discriminating various mental states.Key words:brain-computer interface (BCI), spatiospectral pattern, mental state classification, mind reading, elec-troencephalography (EEG)

Activity Patterns in the Prefrontal Cortex and Hippocampus during and after Awakening from Etomidate Anesthesia

The anesthetic properties of etomidate are largely mediated by gamma-aminobutyric acid type A receptors. There is evidence for the existence of gamma-aminobutyric acid type A receptor subtypes in the brain, which respond to small concentrations of etomidate. After awakening from anesthesia, these subtypes are expected to cause cognitive dysfunction for a yet unknown period of time. The corresponding patterns of brain electrical activity and the molecular identity of gamma-aminobutyric acid type A receptors contributing to these actions remain to be elucidated. Anesthesia was induced in wild-type and beta3(N265M) knock-in mice by intravenous injection of 10 mg/kg etomidate. Local field potentials were recorded simultaneously in the prefrontal cortex and hippocampus using chronically implanted electrode arrays. Local field potentials were sampled before, during, and after anesthesia. In the prefrontal cortex and hippocampus of wild-type mice, intravenous bolus injection of etomidate evoked isoelectric baselines and subsequent burst suppression. These effects were largely attenuated by the beta3(N265M) mutation. During emergence from anesthesia, power density in the theta band (5-15 Hz) transiently increased in the hippocampus of wild types, but not in the mutants, indicating that this action was caused by the receptors harboring beta3 subunits. In both genotypes, etomidate produced a long-lasting (> 1 h after recovery of righting reflexes) decrease in theta-peak frequency. Significant slowing of theta activity was apparent in the hippocampus and prefrontal cortex. Etomidate-induced patterns of brain activity during deep anesthesia mostly involve actions at beta3 containing gamma-aminobutyric acid type A receptors. During the postanesthesia period, altered theta-band activity indicates ongoing anesthetic action.

Binge Drinking Effects on EEG in Young Adult Humans

Young adult (N = 96) university students who varied in their binge drinking history were assessed by electroencephalography (EEG) recording during passive viewing. Groups consisted of male and female non-binge drinkers (>1 to 5/4 drinks/ounces in under two hours), low-binge drinkers (5/4–7/6 drinks/ounces in under two hours), and high-binge drinkers (≥ 10 drinks/ounces in under two hours), who had been drinking alcohol at their respective levels for an average of 3 years. The non- and low-binge drinkers exhibited less spectral power than the high-binge drinkers in the delta (0–4 Hz) and fast-beta (20–35 Hz) bands. Binge drinking appears to be associated with a specific pattern of brain electrical activity in young adults that may reflect the future development of alcoholism.