EEG Frequency Research Articles

The Role of Maladaptive Plasticity in Modulating Pain Pressure Threshold Post-Spinal Cord Injury

Background: Spinal cord injury (SCI) frequently leads to pain, leading to significant disability. Pain sensitization, a key feature of SCI, is commonly assessed via quantitative sensory testing like the Pressure Pain Threshold (PPT), though the factors influencing PPT changes remain unclear. This study hypothesizes that specific clinical and neurophysiological factors modulate PPT in SCI patients. The primary objective is to identify predictors of PPT in SCI patients. Methods: We conducted a cross-sectional analysis of neurophysiological, clinical, and demographic data from 102 SCI patients in an ongoing prospective cohort study called “Deficit of Inhibition as a Marker of Neuroplasticity” (DEFINE study). Multivariable regression analyses were used to evaluate demographic, clinical, and functional variables associated with PPT, the primary outcome measure. Results: The sample comprised 87.9% males with an average age of 41. Trauma was the leading cause of SCI (77.45%), predominantly affecting the cervical and thoracic levels. Pain was reported by 44% of participants, and the mean PPT was 8.3 kPa, measured bilaterally. Multivariate analysis of PPT in the left, right, and bilateral thenar regions revealed consistent trends. Significant negative associations were found between bilateral PPT and low beta EEG frequency in the central area (β = −14.94, p = 0.017), traumatic lesion etiology (β = −1.99, p = 0.038), and incomplete lesions by the American Spinal Injury Association classification (β = −1.68, p = 0.012). In contrast, positive associations were observed with age (β = 0.08, p < 0.001). Conclusions: Our findings show that increased beta oscillations and traumatic brain injury having a lower PPT indicate that factors associated with maladaptive plasticity are associated with decreased and likely less functional PPT. On the other hand, increased motor function may help to regulate PPT in a more functional status.

Characterization of Spatial Cognitive EEG Signals Using Normalized Adjusted Permutation Conditional Mutual Information

Spatial cognitive ability, a fundamental domain within the human cognitive system, involves the perception, interpretation, and manipulation of spatial environments. This study introduces a new EEG feature extraction algorithm, Normalized Adjusted Permutation Conditional Mutual Information (NAPCMI), to improve the accuracy of spatial cognition assessments. By capturing the symmetry and temporal dependencies within EEG signals during spatial cognition tasks, NAPCMI enhances the ability to extract relevant features. The study validates NAPCMI using a BCI-VR spatial cognition assessment system, incorporating gesture recognition. Results demonstrate that NAPCMI outperforms traditional methods in feature extraction, highlighting its potential for advancing the understanding and assessment of spatial cognitive abilities. The findings also emphasize the significance of specific EEG frequency bands, such as Delta and Beta1, in spatial cognition tasks, further validating NAPCMI’s effectiveness.

Mind in others' shoes: Neuroscientific protocol for external referent decision awareness (ERDA) in organizations.

This study investigates the neural and physiological mechanisms underlying External Referent Decision Awareness (ERDA) within organizational contexts, focusing on hierarchical roles (Head, Peer, Staff). Twenty-two professionals participated, and electroencephalographic (EEG frequency band: Delta, Theta, Alpha, Beta, Gamma) and autonomic indices (skin conductance and cardiovascular indices) were recorded, while personality traits and decision-making styles were assessed. Results revealed higher Delta and Theta activation in the left temporo-parietal junction (TPJ) during Peer-related decisions, reflecting increased social cognition and ambiguity regulation in those contexts. Gamma activity, associated with high-order cognitive processes, was prominent in the left frontal cortex across all roles, indicating complex decision evaluation. These findings underscore the complexity of low-frequency bands (Delta and Theta), involved in emotional regulation and social cognition, while high-frequency bands (Gamma) reflect cognitive integration and decision complexity. Furthermore, autonomic data showed higher Skin Conductance Levels (SCL) for Head decisions, suggesting greater emotional involvement.The findings revealed a significant negative correlation between avoidant decision-making styles and the neural and behavioral evaluations of leader decisions, suggesting reduced engagement of neurocognitive systems involved in reward processing and evaluative judgment in individuals with a tendency to avoid decision-making. Additionally, higher extraversion correlated with more favorable evaluations of decisions made by Staff, potentially indicating greater activation in neural circuits associated with social reward and group dynamics. In conclusion, these findings suggest that neural activity and personality traits interact to shape hierarchical decision-making awareness, highlighting the need for tailored leadership and decision-making strategies in organizations.

Gestational Duration and Postnatal Age-Related Changes in Aperiodic and Periodic Parameters in Neonatal and Toddler Electroencephalogram (EEG).

The brain develops most rapidly during pregnancy and early neonatal months. While prior electrophysiological studies have shown that aperiodic brain activity undergoes changes across infancy to adulthood, the role of gestational duration in aperiodic and periodic activity remains unknown. In this study, we aimed to bridge this gap by examining the associations between gestational duration and aperiodic and periodic activity in the EEG power spectrum in both neonates and toddlers. This cross-sectional study involved EEG data from 73 neonates (postnatal age 1-5 days, 40 females) and 56 toddlers (postnatal age of 2.9-3.2 years, 28 females) from the FinnBrain Birth Cohort Study. EEG power spectra were parameterized to aperiodic and periodic components using the SpecParam tool. We tested the associations between gestational duration as well as postnatal age and SpecParam parameters in neonates and toddlers while including birth weight and child sex as covariates. For neonates, multilevel models were employed, considering different data acquisitions (sleep and auditory paradigm + sleep), while in toddlers, regression models were used as only data from the auditory paradigm was available. We found that longer gestational duration was associated with a steeper power spectrum across EEG frequencies both in neonates and toddlers. Effect was especially strong in toddlers (β = 0.45, p = 0.004), while in neonates, it remained nearly statistically significant (p = 0.061). In neonates, a quadratic association between gestational duration and beta center frequency (12.5-30 Hz) was found. In toddlers, beta center frequencies were overall higher in females compared to males. Offset (calculated as the power of the aperiodic curve at 2.5 Hz) and theta center frequency had negative associations with postnatal age in neonates, but not in toddlers. Our results suggest that gestational duration may have significant and relatively long-lasting effects on brain physiology. The possible behavioral and cognitive consequences of these changes are enticing topics for future research.

Depression of cortical neuronal activity after a low-dose fentanyl in preterm infants.

Opioids might be harmful to the developing brain and dosing accuracy is important. We aimed at investigating fentanyl effects on cortical activity in infants using computational re-analysis of bedside recorded EEG signals. Fifteen infants born at median 26.4 gestational weeks (range 23.3-34.1), with a birth weight 740 grams (530-1420) and postnatal age 7 days (5-11) received fentanyl 0.5 or 2 μg/kg intravenously before a skin-breaking procedure or tracheal intubation, respectively. Cortical activity was continuously recorded using amplitude-integrated electroencephalography (aEEG). Analyses using three computational EEG features representing cortical synchrony and signal power, were conducted five minutes pre- and 10 minutes post the drug administration. Visual assessment of trends displayed from the EEG metrics did not indicate systematic changes. However, the magnitude of the changes in the parietal and right hemisphere signals after the dose was significantly correlated (ρ < -0.5, p < 0.05) to the EEG amplitude and frequency power level before drug administration. This effect started after 3-4 min. Fentanyl, even in small doses, may affect cortical activity in the preterm brain. The effect is robustly related to the state of cortical activity prior to drug treatment, which must be taken into account when analysing the effects of sedative drugs.

Classification of obstructive sleep apnea using bio-control feedback EEG biosensor device

Obstructive sleep apnea (OSA) is a common sleeping disorder that can result in shortness of breath due to the relaxation of upper respiratory tract muscles while the patient is asleep. This leads to low blood oxygen saturation and, hence, apnea. OSA is associated with higher risks of hypertension, coronary artery disease, arrhythmias, cardiac failure, and stroke, affecting 5% of the global human population. Thus, early detection and treatment of OSA are crucial. Nowadays, OSA is diagnosed by analyzing EEG signals. Electroencephalographic (EEG) signals are important for monitoring brain activity; this paper focuses on patients with OSA. Unfavorable spikes in the graph, such as head movements, electrical interferences, or even muscle spasms, may interfere with the general EEG frequencies being targeted. Our research was centered on designing a cost-effective electronic circuitry for this device that would efficiently acquire and process EEG signals. We used various methods to improve the device's functionality and accuracy, including rectifications in noise reduction, signal filtering, and structural correction through ICA decomposition. ⁤⁤Our system, theoretically, achieved a high accuracy rate of 97.14% using Support Vector Machines.

Prophylactic Fetal Creatine Supplementation Improves Post-Asphyxial EEG Recovery and Reduces Seizures in Fetal Sheep: Implications for Hypoxic-Ischemic Encephalopathy.

Hypoxic-ischemic encephalopathy (HIE) is a major cause of perinatal brain injury. Creatine is a dietary supplement that can increase intracellular phosphocreatine to improve the provision of intracellular adenosine triphosphate (ATP) to meet the increase in metabolic demand of oxygen deprivation. Here, we assessed prophylactic fetal creatine supplementation in reducing acute asphyxia-induced seizures, disordered electroencephalography (EEG) activity and cerebral inflammation and cell death histopathology. Fetal sheep (118 ± 1 days' gestational age [dGA]; 0.8 gestation) were implanted with electrodes to continuously record EEG and nuchal electromyogram activity. At 121 dGA, fetuses were randomly assigned to sham control (i.v. saline infusion without umbilical cord occlusion [UCO]; SalCon), continuous i.v. creatine infusion (6 mg/kg/h; CrUCO) or isovolumetric saline (SalUCO) followed by UCO at 128 ± 2 dGA that lasted until the mean arterial blood pressure reached 19 mmHg. Brain tissue was collected for histopathology after 72 hours of recovery. Creatine supplementation had no effects on basal systemic or neurological physiology. UCO duration did not differ between CrUCO and SalUCO. After reperfusion, CrUCO fetuses had improved EEG power and frequency recovery and reduced electrographic seizure incidence (SalUCO, 86% vs CrUCO, 29%) and burden. At 72 hours after UCO, cell death in the cerebral cortex and astrogliosis in the periventricular white matter were reduced in CrUCO fetuses compared with SalUCO. Creatine supplementation reduced post-asphyxial seizures and improved EEG recovery. Improvements in functional recovery with creatine were associated with regional reductions in cell death and astrogliosis. Prophylactic creatine treatment has the potential to mitigate functional indices of HIE in the late gestation fetal brain. ANN NEUROL 2024.

Exploring neurofeedback as a therapeutic intervention for subjective cognitive decline.

This study addresses the pressing issue of subjective cognitive decline in aging populations by investigating neurofeedback (NFB) as a potential early therapeutic intervention. By evaluating the efficacy of individualised NFB training compared to standard protocols, tailored to each participant's EEG profile, it provides novel insights into personalised treatment approaches. The incorporation of innovative elements and rigorous analytical techniques contributes to advancing our understanding of NFB's modulatory effects on EEG frequencies and cognitive function in aging individuals. In the context of an aging population, concerns surrounding memory function become increasingly prevalent, particularly as individuals transition into middle age and beyond. This study investigated neurofeedback (NFB) as a potential early therapeutic intervention to address subjective cognitive decline (SCD) in aging populations. NFB, a biofeedback technique utilising a brain-computer interface, has demonstrated promise in the treatment of various neurological and psychological conditions. Here, we evaluated the efficacy of individualised NFB training, tailored to each participant's EEG profile, compared to a standard NFB training protocol aimed at increasing peak alpha frequency power, in enhancing cognitive function among individuals experiencing SCD. Our NFB protocol incorporated innovative elements, including the implementation of a criterion for learning success to ensure consistent achievement levels by the conclusion of the training sessions. Additionally, we introduced a non-learner group to account for individuals who do not demonstrate the expected proficiency in NFB regulation. Analysis of electroencephalographic (EEG) signals during NFB sessions, as well as before and after training, provides insights into the modulatory effects of NFB on EEG frequencies. Contrary to expectations, our rigorous analysis revealed that the ability of individuals with SCD to modulate EEG signal power and duration at specific frequencies was not exclusive to the intended frequency target. Furthermore, examination of EEG signals recorded using a high-density EEG showed no discernible alteration in signal power between pre- and post-NFB training sessions. Similarly, no significant effects were observed on questionnaire scores when comparing pre- and post-NFB training assessments.

P029 High-definition neurostimulation to enhance slow oscillations during sleep in obstructive sleep apnea: A randomised, single-blind, sham-controlled, crossover proof-of-concept protocol

Abstract Global and regional deficits in slow frequency EEG oscillations during non-rapid eye movement sleep, which have been reported in people with obstructive sleep apnea (OSA) may have detrimental consequences on cognitive performance. Current OSA treatments have variable effects on restoring slow waves and improving cognition, and some treatments are poorly tolerated. Previous studies using non-invasive transcranial direct current stimulation (tDCS) applied to frontal brain regions enhanced slow wave oscillations in healthy adults and may have utility in an OSA population. This protocol will assess the feasibility of using tDCS to enhance slow oscillations in middle to late aged people with OSA. A randomised, crossover design will test two active (maximum current density 0.522mA/cm2) and one sham (&amp;lt;0.1mA) slow oscillation tDCS conditions. In this proof-of-concept study, 5 participants aged 40-65 years with mild-moderate OSA will attend three in laboratory visits with high-density EEG, with a 1-week washout. Active or sham stimulation in frontal or parietal regions will be applied in 5 x 5-minute blocks during the first NREM period of a 90-minute nap. EEG data will be measured for 60 seconds after each stimulation block. Memory and attention tasks and a perception questionnaire will be completed before and after the nap. The primary outcome is technical feasibility. Secondary outcomes include protocol acceptability, blinding and perceived effectiveness and delta EEG power. Tertiary outcomes include visuo-spatial recognition, vigilance, sigma power, and sleep macro-architecture. Preliminary feasibility data on this novel intervention to target sleep and cognition in OSA will inform a larger clinical trial.

Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features

Accurate monitoring of the depth of anesthesia (DoA) is essential for ensuring patient safety and effective anesthesia management. Existing methods, such as the Bispectral Index (BIS), are limited in real-time accuracy and robustness. Current methods have problems in generalizability across diverse patient datasets and are sensitive to artifacts, making it difficult to provide reliable DoA assessments in real time. This study proposes a novel method for DoA monitoring using EEG signals, focusing on accuracy, robustness, and real-time application. EEG signals were pre-processed using wavelet denoising and discrete wavelet transform (DWT). Features such as Permutation Lempel–Ziv Complexity (PLZC) and Power Spectral Density (PSD) were extracted. A random forest regression model was employed to estimate anesthetic states, and an unsupervised learning method using the Hurst exponent algorithm and hierarchical clustering was introduced to detect transitions between anesthesia states. The method was tested on two independent datasets (UniSQ and VitalDB), achieving an average Pearson correlation coefficient of 0.86 and 0.82, respectively. For the combined dataset, the model demonstrated an R-squared value of 0.70, a RMSE of 6.31, a MAE of 8.38, and a Pearson correlation of 0.84, showcasing its robustness and generalizability. This approach offers a more accurate and reliable real-time DoA monitoring tool that could significantly improve patient safety and anesthesia management, especially in diverse clinical environments.

Phase synchronization analysis of EEG functional connectivity in Parkinson’s disease

There is a growing need for research on Parkinson’s disease (PD), a neurological condition that often affects the elderly. By examining brain network connectivity, researchers are able to discover how different brain regions interact during various cognitive and behavioral tasks. They can also understand how changes in nonlinear connections may be linked to neurological and mental illnesses. In this paper, the synchrony levels of 59 EEG channels from 26 Parkinson’s patients and 13 healthy subjects is examined by utilizing Phase-Lag Index (PLI) during a motor task and resting-state conditions. Examining different EEG frequency bands shows that whole-brain synchronization in the delta band is significantly lower in PD patients compared to healthy subjects during the task. PD patients also exhibit a lower clustering coefficient and a higher shortest path length in this band during the task, which shows the higher small-worldness in Parkinson’s patients compared to healthy individuals. Moreover, the global efficiency in the delta band is significantly reduced in PD patients during the task. An analysis of local efficiency shows that PD and control groups differ in 57 channels. These results reveal that Parkinson’s patients appear to have considerable pathological abnormalities in their delta band connectivity and its characteristic features.

Gamma oscillation optimally predicts finger movements

Our fingers are the most dexterous and complicated parts of our body and play a significant role in our daily activities. Non-invasive techniques, such as Electroencephalography (EEG) and Electromyography (EMG) can be used to collect neural and muscular signals related to finger movements. In this study, we combined an 8-channel EMG and a 31-channel EEG while the human subject moved one of the five fingers on the right hand. To identify the best EEG frequency features that encode distinct finger movements, we systematically examined the decoding accuracies of the slow-cortical potentials and three types of sensorimotor rhythms, namely the Mu, beta, and gamma oscillations. For both EMG and EEG, we came up with a simple and unified root mean square or power approach that avoided the complex signal features used by previous studies. The signal features were then fed into a feedforward artificial-neural-network (ANN) classifier. We found that the low-gamma oscillation provided the best decoding performance over the other frequency bands, ranging from 65.0 % to 89.0 %, which was comparable to the EMG performance. Combining EMG and low gamma into a single ANN can further improve the outcome for subjects who had showed suboptimal performances with EMG or EEG alone. This study provided a simple and efficient algorithm for prosthetics that assist patients with sensorimotor impairments.

EEG frequency tagging reveals the integration of dissimilar observed actions

Extensive research has demonstrated that visual and motor cortices can simultaneously represent multiple observed actions. This ability undoubtedly constitutes a crucial ingredient for the understanding of complex visual scenes involving different agents. However, it is still unclear how these distinct representations are integrated into coherent and meaningful percepts. In line with studies of perceptual binding, we hypothesized that similar movements would be more easily integrated. To test this hypothesis, we developed an EEG frequency tagging experiment in which two hand movements were displayed simultaneously at two different presentation rates. Crucially, the degree of similarity between the two movements varied along two dimensions, namely action identity (i.e., same or different performed movement), and agent identity (i.e., one agent performing a bimanual movement, or two agents moving each one hand). Contrary to our predictions, we found a larger intermodulation oscillatory component, indexing the integrated processing of the two individual movements, when they were less similar. We propose that integration-by-dissimilarity might serve as a top-down process to solve conflict caused by incongruent movements, thus contributing to the global understanding of distinct moving individuals in a complex social scene.

Meta-analysis on QEEG Changes to Antidepressant Treatment Among Patients with Depression

Introduction: Diagnostic and treatment accuracy of depression can lead to a better and possibly earlier response and remission in patients. The literature, though scanty, seems to suggest that quantitative electroencephalography (QEEG) can predict the outcome of antidepressant effects. Methodology: Articles published between January 1990 and July 2019, including those dealing with QEEG recordings before and after the initiation of antidepressant medication, were included. The pooled effect size and subgroup analysis of waveforms were calculated to predict response to antidepressants. Result: In all, 572 results were retrieved from the searches, of which 20 studies were included. Pooled data using a random-effects model (REM) calculated an effect size of 0.80 (95% CI [0.64–0.97]). Heterogeneity of the sample was low with Tau² = 0.02; df = 18 ( P = .30); I² = 12%. Moreover, subgroup analysis showed that theta band frequencies were better at predicting response than alpha band frequencies (the standard mean difference [SMD] for theta was 0.91 compared to 0.68 for alpha waves). Conclusions: QEEG is a valuable predictor of the antidepressant response. Among the EEG frequencies, the theta band showed the most significant change with treatment.

EEG Spectral Power Changes in Patients With Dysexecutive Syndrome Following Cognitive Intervention.

Acquired brain injury (ABI) leads to cognitive deficiencies, alteration of brain activity associated with an increase in slow-wave (delta and theta bands) power, and reduced fast-wave (alpha, beta, and gamma bands) power. To compensate for the cognitive deficits that impact autonomy and quality of life, patients in a chronic phase can benefit from cognitive intervention. This study explores the effects of cognitive intervention on brain activity, measured by electroencephalography (EEG), and on executive functioning, assessed by the Test of Attentional Performance (TAP) battery. We provided an ecological rehabilitation intervention, simulating real-life tasks adapted for patients with chronic cognitive disorders. A single-case experimental design (SCED) assessed patients' performance in terms of correct responses percentage (CRs) and reaction times (RTs), and EEG spectral powers before and 1 month after the intervention. The TAP tasks included working memory (WM), divided attention (DA), inhibition (GO), and flexibility (FL). EEG frequency powers were also measured during resting states. One month after the intervention, significant improvements were observed in CRs and RTs for the FL task. Increases in all frequency band powers occurred during FL, WM, and DA tasks, except for alpha bands in DA. In the GO task, delta and gamma power also increased after the intervention. No significant changes were found during resting-state EEG. The results of this open study, without a control group, are preliminary. The effects of the therapy are mostly reflected by changes in mental FL performance and altered EEG patterns during cognitive tasks, particularly in slow and fast-frequency bands. We argue that cognitive intervention could amplify the compensatory mechanisms following brain damage and/or ease restoration mechanisms in the fast-frequency activity bands. Further SCEDs or studies with control groups are needed to confirm these findings and the role of EEG biomarkers in rehabilitation.

Neural encoding of melodic expectations in music across EEG frequency bands.

The human brain tracks regularities in the environment and extrapolates these to predict future events. Prior work on music cognition suggests that low-frequency (1-8Hz) brain activity encodes melodic predictions beyond the stimulus acoustics. Building on this work, we aimed to disentangle the frequency-specific neural dynamics linked to melodic prediction uncertainty (modelled as entropy) and prediction error (modelled as surprisal) for temporal (note onset) and content (note pitch) information. By using multivariate temporal response function (TRF) models, we re-analysed the electroencephalogram (EEG) from 20 subjects (10 musicians) who listened to Western tonal music. Our results show that melodic expectation metrics improve the EEG reconstruction accuracy in all frequency bands below the gamma range (< 30 Hz). Crucially, we found that entropy contributed more strongly to the reconstruction accuracy enhancement compared to surprisal in all frequency bands. Additionally, we found that the encoding of temporal, but not content, information metrics was not limited to low frequencies, rather it extended to higher frequencies (> 8Hz). An analysis of the TRF weights revealed that the temporal predictability of a note (entropy of note onset) may be encoded in the delta- (1-4Hz) and beta-band (12-30 Hz) brain activity prior to the stimulus, suggesting that these frequency bands associate with temporal predictions. Strikingly, we also revealed that melodic expectations selectively enhanced EEG reconstruction accuracy in the beta band for musicians, and in the alpha band (8-12 Hz) for non-musicians, suggesting that musical expertise influences the neural dynamics underlying predictive processing in music cognition.

Quantitative EEG biomarkers for STXBP1-related disorders.

EEG patterns and quantitative EEG (qEEG) features have been poorly explored in monogenic epilepsies. Herein, we investigate regional differences in EEG frequency composition in patients with STXBP1 developmental and epileptic encephalopathy (STXBP1-DEE). We conducted a retrospective study collecting electroclinical data of patients with STXBP1-DEE and two control groups of patients with DEEs of different etiologies and typically developing individuals matched for age and sex. We performed a (1) visual EEG assessment, (b) qEEG analysis, and (c) electrical source imaging (ESI). We quantified the relative power (RP) of four frequency bands (α β, θ, δ), in two electrode groups (anterior/posterior), and compared their averages and dynamics (standard deviation [SD] over time). The ESI was performed by applying the standard Distributed Source Modeling algorithm. We analyzed 42 EEG studies in 19 patients with STXBP1-DEE (10 female), with a median age at recordings of 9.6 years (range 9 months to 29 years). The δRP was higher in recordings of STXBP1-DEE (p < .001) compared to both control groups, suggesting the pathogenicity and STXBP1-specificity of these findings. In STXBP1-DEE, the δRP was significantly higher in the anterior electrode group compared to the posterior one (p = .003). There was no correlation between the anterior δRP and the epilepsy focus, age at recordings, and concomitant medications The ESI modeling of this activity showed a widespread involvement of the dorsomesial frontal cortex, suggesting a large corticosubcortical pathologic network. Finally, we identified two groups of recordings: cluster.1 with higher anterior δRP and low dynamics and cluster.2 with lower δRP and higher dynamics. Patients in cluster.1 had a more severe epilepsy and neurological phenotype compared to patients in cluster 2. The qEEG analysis showed a predominant frontal slow activity as a specific STXBP1 feature that correlates with the severity of the phenotype and may represent a biomarker for prospective longitudinal studies of STXBP1-DEE.

Investigating Brain Responses to Transcutaneous Electroacupuncture Stimulation: A Deep Learning Approach

This study investigates the neurophysiological effects of transcutaneous electroacupuncture stimulation (TEAS) on brain activity, using advanced machine learning techniques. This work analyzed the electroencephalograms (EEG) of 48 study participants, in order to analyze the brain’s response to different TEAS frequencies (2.5, 10, 80, and sham at 160 pulses per second (pps)) across 48 participants through pre-stimulation, during-stimulation, and post-stimulation phases. Our approach introduced several novel aspects. EEGNet, a convolutional neural network specifically designed for EEG signal processing, was utilized in this work, achieving over 95% classification accuracy in detecting brain responses to various TEAS frequencies. Additionally, the classification accuracies across the pre-stimulation, during-stimulation, and post-stimulation phases remained consistently high (above 92%), indicating that EEGNet effectively captured the different time-based brain responses across different stimulation phases. Saliency maps were applied to identify the most critical EEG electrodes, potentially reducing the number needed without sacrificing accuracy. A phase-based analysis was conducted to capture time-based brain responses throughout different stimulation phases. The robustness of EEGNet was assessed across demographic and clinical factors, including sex, age, and psychological states. Additionally, the responsiveness of different EEG frequency bands to TEAS was investigated. The results demonstrated that EEGNet excels in classifying EEG signals with high accuracy, underscoring its effectiveness in reliably classifying EEG responses to TEAS and enhancing its applicability in clinical and therapeutic settings. Notably, gamma band activity showed the highest sensitivity to TEAS, suggesting significant effects on higher cognitive functions. Saliency mapping revealed that a subset of electrodes (Fp1, Fp2, Fz, F7, F8, T3, T4) could achieve accurate classification, indicating potential for more efficient EEG setups.

Abnormal EEG Effects of Acute Apomorphine Injection in 5xFAD Transgenic Mice Are Partially Normalized in Those Chronically Pretreated with Apomorphine: The Time-Frequency Clustering of EEG Spectra.

In experimental and clinical studies of pharmacological treatments for Alzheimer's disease (AD), the electroencephalogram (EEG) frequency spectrum approach has demonstrated its efficacy in determining the characteristics of pathological changes in the functioning of different cerebral structures, interconnections between them, and disturbances in the brain neurotransmitter systems. The main results have been obtained in frames of traditionally used so-called "classical" EEG frequency bands: delta, theta, alpha, and beta. This unified approach simplifies comparing data from different studies but loses the dynamic peculiarities of the effects because of their time-dependent transition through the borders of the "classical" bands. In this study on non-narcotized freely moving 5xFAD transgenic mice, a model of AD, chronically pretreated with a non-selective dopamine (DA) receptor agonist, apomorphine (APO), we analyze the transitory EEG effects of acute APO injection in different brain areas by use of our "time-frequency" clustering program. The acute injection of APO was used to compare DA receptor sensitivity in 5xFAD mice pretreated with either APO or saline vs. wild-type (WT) mice pretreated with saline. After acute APO injection, the clusters of enhanced EEG activity centered in the theta-alpha frequency range observed in WT mice disappeared in 5xFAD mice pretreated with saline and practically recovered in 5xFAD mice pretreated with APO. In 5xFAD mice pretreated with saline, the sensitivity of DA receptors was disturbed; chronic APO pretreatment mainly recovered this characteristic in 5xFAD mice. The "clustering" of pharmacological EEG effects and their time-dependent transition between classical frequency bands is a new effective approach for analyzing cerebral neurotransmission in neurodegenerative pathologies.

Long-term administration of paroxetine increases cortical EEG beta and gamma band activities in healthy awake rats

Understanding the electrophysiological properties of antidepressant medications is important to resolve the response heterogeneity of these drugs in clinical practice. Administration of paroxetine, a selective serotonin reuptake inhibitor, has been shown to increase serotonin levels that affect cortical activities in healthy subjects. However, the extent to which cortical oscillations can be altered by ongoing administration of paroxetine is not known. Here, we develop EEG biomarkers showing long-term effects of paroxetine. EEG changes were analyzed using Neuroscan in healthy wakeful rats administered paroxetine (4 mg/kg/day) for six weeks. Subsequent EEG recordings taken at 3 and 6 weeks after treatment showed differences in cortical oscillations obtained from both hemispheres and frontal-central-parietal regions. Chronic paroxetine administration resulted in an increase in gamma band activity. Comparison of EEG frequency bands of paroxetine and saline groups showed an enhancement in higher frequency activities at third weeks after the treatment. Higher activity of alpha oscillations in the temporal cortex was persistent at sixth week of the administration. Overall, our results suggest that chronic paroxetine administration affects cortical oscillations across an expansive network.