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Sleep bruxism is an involuntary, exaggerated jaw-closing activity during sleep. Selective serotonin reuptake inhibitor (SSRI) use is a risk factor for bruxism. However, the effect of various SSRIs on masseter (jaw-closing) muscle activity remains unclear. Here, we examined the effects of long-term administration of two SSRIs, fluoxetine (FLX) and paroxetine (PRX), for 14 days on masseter muscle activity during wakefulness, non-rapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep for 24 h in mice. Vigilance states were scored based on electroencephalographic, electrooculography and neck electromyographic (EMG) activities. The EMG activity of the masseter muscle was quantified in 6 h periods. FLX and PRX did not affect the duration of the three vigilance states. Both drugs significantly prolonged the REM sleep episode duration while decreasing the number of episodes. FLX significantly increased REM sleep onset latency. Neither FLX nor PRX affected the mean masseter EMG activity during wakefulness. FLX significantly increased the relative time of masseter muscle activity in NREM sleep during 02:00–08:00 and 08:00–14:00, while PRX did not affect three vigilance states. Overall, FLX had a limited but significant effect on masseter muscle activity in NREM sleep during specific periods.

Background: 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 needed. Methods: 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) activation (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. Results: MEP amplitudes elicited in real-time during classifier-predicted personalized strong CST states were significantly larger than those elicited during corresponding weak and random CST states. MEP amplitudes elicited in real-time during classifier-predicted personalized strong CST states were also significantly less variable than those elicited during corresponding weak CST states. Personalized CST states lasted for ∼1–2 s at a time and ∼1 s elapsed between consecutive similar states. Individual participants exhibited unique differences in spectro-spatial EEG patterns between classifier-predicted personalized strong and weak CST states. Conclusion: 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.

Background:: Deep brain stimulation of the central thalamus (CT-DBS) has potential for modulating states of consciousness, but it can also trigger electrographic seizures, including poly-spike-wave trains (PSWT). Objectives:: To report the probability of inducing PSWTs during CT-DBS in awake, freely-moving mice. Methods:: Mice were implanted with electrodes to deliver unilateral and bilateral CT-DBS at different frequencies while recording electroencephalogram (EEG). We titrated stimulation current by gradually increasing it at each frequency until a PSWT appeared. Subsequent stimulations to test arousal modulation were performed at the current one step below the current that caused a PSWT during titration. Results:: In 2.21% of the test stimulations (10 out of 12 mice), CT-DBS caused PSWTs at currents lower than the titrated current, including currents as low as 20 μA. Conclusion:: Our study found a small but significant probability of inducing PSWTs even after titration and at relatively low currents. EEG should be closely monitored for electrographic seizures when performing CT-DBS in both research and clinical settings.

We report a case of a Chinese girl who presented with multiple seizure types of epilepsy, followed by motor and intellectual regression, vision impairment, and cerebral and cerebellar atrophy. She carries an unreported compound heterozygous variant of the ASAH1 gene and is diagnosed with spinal muscular atrophy associated with progressive myoclonic epilepsy (SMA-PME), a disorder in which ceramide accumulation in lysosomes due to a decrease in acid ceramidase activity. This case suggests attention to this rare class of deceases involving both the central and peripheral nervous systems.

The rapidly increasing number of elderly people in the world highlights the need for the development of innovative foods with modified textures that do not expose the elderly to the risks associated with food consumption (risk of aspiration, suffocation, and chocking). Providing specific food such as edible gel for the elderly population and the study of their properties is a challenge for the scientific community. There are some available gels in the supermarkets destined for the sports population, with specific texture and technological properties that could be used and extrapolated for senior people. To explore this potential, five types of sport commercial gels purchased from a local Slovak market were characterized in order to evaluate their technological properties and to know if these types of gels are suitable for the senior population. The energy gels were evaluated using acceptance testing, involving 75 seniors who evaluated important organoleptic attributes by a combination of hedonic and intensity scales. The same consumer panel then profiled the gels using the Temporal Dominance of Sensations (TDS) technique. The prevalence of food neophobia was measured with the Food Neophobia Scale (FNS) and also using neuroimaging and biometric methods. The results suggest that there are significant differences in the perception of edible gels, as confirmed by measurements via electroencephalography (EEG) and Facereading. We conclude by suggesting the potential of specific foods such as edible gels for the elderly population as our findings also confirm that the composition of these specific and sustainable foods may elicit different perceptions. This highlights the need to use biometric and neuroimaging methods in food research in order to create more optimal formulations for specific populations.

Background: Cortical high-frequency activation immediately before death has been reported, raising questions about an enhanced conscious state at this critical time. Here, we analyzed an electroencephalogram (EEG) from a comatose patient during the dying process with a standard bedside monitor and spectral parameterization techniques. Methods: We report neurophysiologic features of a dying patient without major cortical injury. Sixty minutes of frontal EEG activity was recorded using the Sedline™ monitor. Quantitative metrics of the frequency spectrum, the non-oscillatory 1/f characteristic, and signal complexity with Lemple-Ziv-Welch and permutation entropy were calculated. In addition to comparing the EEG trajectories over time, we provide a comparison to EEG records obtained from other studies with well-known vigilance states (sleep, anesthesia, and wake). Results: Although we observed changes in high-frequency activation during the dying process, larger alterations of the aperiodic EEG components were also noted. These changes differed dramatically when compared to EEG records representative of wake, slow-wave sleep, or anesthesia. Although still fundamentally unique, the neuronal activity present in the dying brain is more similar to REM sleep than any other state we tested. Conclusion: Even in patients with coma, temporal dynamics in quantitative EEG features (including the aperiodic components) can be observed in the final hour before death.

Background: Maintaining an appropriate depth of anaesthesia is important for avoiding adverse effects from undermedication or overmedication during surgery. Electroencephalography (EEG) has become increasingly used to achieve this balance. Investigating the predictive power of intracranial EEG (iEEG) and scalp EEG for different levels of sedation could increase the utility of EEG monitoring. Methods: Simultaneous iEEG, scalp EEG, and Observer's Assessment of Alertness/Sedation (OAA/S) scores were recorded during emergence from anaesthesia in seven patients undergoing placement of intracranial electrodes for medically refractory epilepsy. A deep learning model was constructed to predict an OAA/S score of 0–2 vs 3–5 using iEEG, scalp EEG, and their combination. An additional five patients with only scalp EEG data were used for independent validation. Models were evaluated using the area under the receiver-operating characteristic curve (AUC). Results: Combining scalp EEG and iEEG yielded significantly better prediction (AUC=0.795, P

Objective: To investigate the effects of sensory reweighting on postural control and cortical activity in individuals with Parkinson's disease (PD) compared to age-matched controls using a virtual reality sensory organization test (VR-SOT). Design: Cross-sectional pilot study. Setting: University research laboratory. Participants: Ten participants with idiopathic Parkinson's disease and 11 age- and sex-matched control participants without neurologic disorders. Interventions: Not applicable. Main Outcome Measures: Changes in center of pressure (COP) and electroencephalography (EEG) activity (ie, power) in the alpha band and the theta/beta ratio recorded during the VR-SOT were the main outcome variables. Results: PD participants exhibited greater COP displacement, particularly in the mediolateral direction across sensory conditions. They also showed increased alpha power when relying on visual inputs and increased theta/beta ratio power when depending on somatosensory inputs. Conclusion: PD affects sensory reweighting mechanisms involved in postural control, as evidenced by greater COP displacement and altered cortical activity. These findings emphasize the potential of EEG and VR-SOT in understanding and monitoring postural control impairments in PD.

Visual attention is a type of selective attention that plays an important role in prioritizing and processing the information received from the visual scenes around us. The brain is an intricate network composed of numerous regions, each with distinct functions. As different tasks are performed, brain regions regularly synchronize and correlate with each other through intricate networks of connections. The aim of this study is to decode two states of attention by examining the interactions and connections between different brain regions using graph theory. This is demonstrated by EEG recordings from 15 participants who performed visual attention task. Pearson's correlation coefficient and coherence have been used to measure the functional connections between brain regions. In fact, each of these two criteria is regarded as an individual feature, and we perform decoding using each criterion separately. With an optimal selection of 40 connectivity features, the QDA classifier attained accuracies of 79.83% and 83.28% using correlation and coherence features, respectively. The results of attention decoding using the coherence criterion are more promising, indicating the superior effectiveness of coherence-based methods. Therefore, this study employed graph theory to analyse a neural network derived from coherence measurements. The study focused on three graph-theoretical metrics: degree centrality, efficiency, and betweenness centrality. The QDA classifier, using an optimal set of 40 features that includes degree, betweenness, and channel efficiency, achieved an accuracy of 86.46%. In comparison, the QDA classifier with 40 features based solely on degree centrality reached an accuracy of 89.96%. Finally, the results of this research indicate that analysing brain connections and brain network graphs can effectively decode different covert visual attention states.

Objective: Electroencephalography (EEG) is widely used for assessing infant's brain activity, and multi-channel recordings support studies on functional cortical networks. Here, we aimed to assess how the number of recording electrodes affects the quality and level of details accessible in studying infant's cortical networks. Methods: Dense array EEG recordings with 124 channels from N=20 infants were used as the reference, and lower electrode numbers were subsampled to simulate recording setups with 63, 31, and 19 electrodes, respectively. Cortical activity networks were computed for each recording setup and different frequencies using amplitude and phase correlation measures. The effects of the recording setup were systematically assessed on global, nodal, and edge levels. Results: Compared to the reference 124-channel recording setup, lowering electrode density affected network measures in a modality- and frequency-specific manner. The global network features were essentially comparable with 63 or 31 channels. However, the analytic reliability of the local network measures, both at nodal and edge levels, was proportional to the electrode density. The low-frequency amplitude correlations were most robust to the number of recording electrodes, whereas higher frequency phase correlation networks were most sensitive to the density of recording electrodes. Conclusions: Our findings suggest strong and predictable effects of recording setup on the network analyses. Higher electrode number supports studies on networks with phase correlations, higher frequency, and finer spatial details. Significance: The relationship between the recording setup and reliability of network analyses is essential for the prospective design of research data collection, as well as for guiding analytic strategies when using already collected EEG data from infants.

The effectiveness of rehabilitation is contingent upon the motor recovery process which typically involves long-term motor skill re-acquisition. Given that the learning process can be modulated by task difficulty, elucidating the underlying neural mechanism is essential for optimizing rehabilitation prescription to suit different patient conditions. This study aimed to investigate the impact of task difficulty on cortical response during force-control training via electroencephalography (EEG). An 8-day visuomotor force-tracking training experiment was conducted. Healthy right-handed participants (N=33) were recruited and randomly assigned to 3 groups, and each group was tasked with a different level of difficulty. The task difficulty was manipulated by variation in force-production complexity and execution sequence assignments, with real-time visual feedback provided to participants for self-output adjustment. Behavioral performance was quantitatively assessed using a pre-defined score metric related to performance accuracy. The EEG signals were collected, and corresponding event-related desynchronization (ERD) and relative functional connectivity (FC) during the task execution were analyzed within the alpha- (8–13 Hz) and beta- (15–30 Hz) bands. A post-training experiment was also performed to evaluate the near-transfer capability of learning. Results showed all the behavioral performances improved during practice, while higher task difficulty level was affiliated with better post-training near-transfer ability. The dynamic neural response to training could be mediated by changes in difficulty level, where increased task complexity corresponded with the heightened activities in the beta-band priorly within the right dorsolateral prefrontal area. Additionally, stronger alpha-band functional connectivity was observed to be predominantly associated with the left motor area (LMA) during challenging tasks, and the intensification in connectivity persisted selectively post-training which appeared to be acritical factor for skill transfer performance improvement. These findings illustrated the dynamic neural mechanism through which task difficulty affects behavioral performance during long-term motor training with accurate force-control purpose. The selectively strengthened functional connectivity may contribute to facilitating new task execution after training interventions. Therefore, beneficial neural modulation can be expected to be feasible by well-designed task difficulty strategies for effective motor rehabilitation.

Summary: Background: Intrapartum-related neonatal encephalopathy (NE) is a leading cause of childhood mortality and morbidity. Continuous electroencephalography (EEG) is gold standard for neonatal brain monitoring; however, low-income country data is lacking. We examined EEG in a Ugandan cohort with NE to describe feasibility, background activity, seizure prevalence and burden, and associations with clinical presentation and outcome. Methods: Neonates with NE were recruited from a single hospital referral centre in Kampala, Uganda (Oct 2019–Oct 2020) and underwent EEG monitoring. Feasibility was assessed as to whether EEG monitoring of diagnostic quality could be achieved from days 1–5. Evolution of clinical presentation was assessed by Sarnat classification and daily Thompson score was performed. EEG background severity was graded at 12, 24, 48 and 72 h after birth, and at time of Thompson score. Seizures were annotated remotely by experts and assessed for frequency, duration, burden, and status epilepticus. Early childhood outcome was assessed at follow up, and adverse outcome defined as death or neurodevelopmental impairment (NDI) at 18–24 months of age. Findings: In this prospective feasibility cohort study, diagnostic quality EEGs were recorded for 50 of 51 recruited neonates (median duration 71.4 h, IQR 52.4–72.2), indicating feasibility. Of 39 participants followed to 18–24 months, 13 died and 7 had NDI. Daily Thompson score and EEG background grade were strongly correlated across all timepoints (days 1–5). Thompson score of ≥7 was most predictive of moderate-severe EEG background abnormality (AUC 0.83). Prognostic accuracy of moderate-severe EEG background grade to predict NDI was high (AUC 0.74). Electrographic seizures were seen in 52% (26); median seizure burden was high at 264 min (IQR 27.8–523.7, range 1.3–1374.1); half (13) had status epilepticus. Interpretation: EEG monitoring was feasible as a research tool in this sub-Saharan Africa setting. EEG background activity correlated strongly with scored neurological assessment and predicted adverse early childhood outcome. Seizure prevalence and burden, including status epilepticus, were high in this uncooled cohort with important potential longer-term implications for survivors. Funding: Bill & Melinda Gates Foundation grant number OPP1210890; Wellcome Trust Innovator award (209325/Z/17/Z).

Background: This study explored a novel multimodal neuroimaging approach to assess neurovascular coupling (NVC) in humans using electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and transcranial Doppler ultrasound (TCD). Methods: Fifteen participants (nine females; age 19–32) completed concurrent EEG-fNIRS-TCD imaging during motor (finger tapping) and visual (“Where's Waldo?”) tasks, with synchronized monitoring of blood pressure, capnography, and heart rate. fNIRS assessed microvascular oxygenation within the frontal, motor, parietal, and occipital cortices, while the middle and posterior cerebral arteries (MCA/PCA) were insonated using TCD. A 16-channel EEG set-up was placed according to the 10–20 system. Wilcoxon signed-rank tests were used to compare physiological responses between the active and resting phases of the tasks, while cross-correlations with zero legs compared cerebral and systemic hemodynamic responses across both tasks. Results: Time-frequency analysis demonstrated a reduction in alpha and low beta band power in electrodes C3/C4 during finger tapping (p < 0.045) and all electrodes during the Waldo task (all p < 0.001). During Waldo, cross-correlation analysis demonstrated the change in oxygenated hemoglobin and cerebral blood velocity had a moderate-to-strong negative correlation with systemic physiological influences, highlighting the measured change resulted from neuronal input. Deoxygenated hemoglobin displayed the greatest negative cross-correlation with the MCA/PCA within the motor cortices and visual during the motor and visual tasks, respectively (range:0.54, -0.82). Conclusions: This investigation demonstrated the feasibility of the proposed EEG-fNIRS-TCD response to comprehensively assess the NVC response within human, specifically quantifying the real-time temporal synchrony between neuronal activation (EEG), microvascular oxygenation changes (fNIRS), and conduit artery velocity alterations (TCD).

Detecting consciousness in clinically unresponsive patients remains a significant challenge. Existing studies demonstrate that electroencephalography (EEG) can detect brain responses in behaviorally unresponsive patients, indicating potential for consciousness detection. However, most of this evidence is based on chronic patients, and there is a lack of studies focusing on acute coma cases. This study aims to detect signs of residual consciousness in patients with acute coma by using bedside EEG and electromyography (EMG) during an auditory oddball paradigm. We recruited patients with acute brain injury (either traumatic brain injury or cardiac arrest) who were admitted to the intensive care unit within two weeks after injury, with a Glasgow Coma Scale (GCS) score of 8 or below. Auditory stimuli included the patients' own names and other common names (referred to as standard names), spoken by the patients’ relatives, delivered under two conditions: passive listening (where patients were instructed that sounds would be played) and active listening (where patients were asked to move hands when heard their own names). Brain and muscle activity were recorded using EEG and EMG during the auditory paradigm. Event-related potentials (ERP) and EMG spectra were analyzed and compared between responses to the subject’s own name and other standard names in both passive and active listening conditions. A total of 22 patients were included in the final analysis. Subjects exhibited enhanced ERP responses when exposed to their own names, particularly during the active listening task. Compared to standard names or passive listening, distinct differences in brain network connectivity and increased EMG responses were detected during active listening to their own names. These findings suggest the presence of residual consciousness, offering the potential for assessing consciousness in behaviorally unresponsive patients.

Background: Prior work has shown that transcranial alternating current stimulation (tACS) of parietooccipital alpha oscillations (8–14 Hz) can modulate working memory (WM) performance as a function of the phase lag to endogenous oscillations. However, leveraging this effect using real-time phase-tuned tACS has not been feasible so far due to stimulation artifacts preventing continuous phase tracking. Objectives and hypothesis: We aimed to develop a system that tracks and adapts the phase lag between tACS and ongoing parietooccipital alpha oscillations in real-time. We hypothesized that such real-time phase-tuned tACS enhances working memory performance, depending on the phase lag. Methods: We developed real-time phase-tuned closed-loop amplitude-modulated tACS (CLAM-tACS) targeting parietooccipital alpha oscillations. CLAM-tACS was applied at six different phase lags relative to ongoing alpha oscillations while participants (N = 21) performed a working memory task. To exclude that behavioral effects of CLAM-tACS were mediated by other factors such as sensory co-stimulation, a second group of participants (N = 25) received equivalent stimulation of the forehead. Results: WM accuracy improved in a phase lag dependent manner (p = 0.0350) in the group receiving parietooccipital stimulation, with the strongest enhancement observed at 330° phase lag between tACS and ongoing alpha oscillations (p = 0.00273, d = 0.976). Moreover, across participants, modulation of frontoparietal alpha oscillations correlated both in amplitude (p = 0.0248) and phase (p = 0.0270) with the modulation of WM accuracy. No such effects were observed in the control group receiving frontal stimulation. Conclusions: Our results demonstrate the feasibility and efficacy of real-time phase-tuned CLAM-tACS in modulating both brain activity and behavior, thereby paving the way for further investigation into brain-behavior relationships and the exploration of innovative therapeutic applications.

Background: The clinical presentations of abusive head trauma can abruptly worsen, so the occurrence of seizures and changes of EEG can be variable according to patients’ conditions. Since the changes of EEG background waves reflect the cortical function of children, we aimed to find out whether the timing of EEG background, epileptiform discharges and seizure patterns were associated with the outcomes of patients with AHT. Material and methods: Using seizure type and acute stage electroencephalographic (EEG) characteristics to assess adverse neurological outcomes in children with seizures secondary to abusive head trauma (AHT). Children who were hospitalized with AHT at a tertiary referral hospital from October 2000 to April 2010 were evaluated retrospectively. A total of 50 children below 6 years of age admitted due to AHT were included. KOSCHI outcome scale was used to evaluate the primary outcome and neurological impairment was used as secondary outcome after 6 months discharge. Results: Children with apnea, cardiac arrest, reverse blood flow and skull fracture in clinic had a higher mortality rate even in the no-seizure group (3/5 [60%] vs. 3/45 [6.7%], odds ratio [OR] = 11; 95% CI = 2.3–52; p = 0.025). Seizure occurrence reduced mostly at the second day after admission in seizure groups; but children with persistent seizures for 1 week showed poor neurological outcomes. The occurrence of initial seizure was frequency associated with younger age; focal seizure, diffuse cortical dysfunction in acute-stage EEG, and low Glasgow Coma Scale (GCS) score were significantly related to poor outcomes after 6 months. Diffuse cortical dysfunction was also associated with motor, speech, and cognitive dysfunction. Conclusions: Diffuse cortical dysfunction in acute-stage EEG combined with low GCS score and focal seizure may related to poor outcomes and neurological dysfunctions in children with AHT.

Background: Virtually everyone is exposed to power-frequency MF (50/60 Hz), inducing in our body electric fields and currents, potentially modulating brain function. MF-induced electric fields within the central nervous system can generate flickering visual perceptions (magnetophosphenes), which form the basis of international MF exposure guidelines and recommendations protecting workers and the general public. However, magnetophosphene perception thresholds were estimated 40 years ago in a small, unreplicated study with significant uncertainties and leaving open the question of the involved interaction site. Methods: We used a stimulation modality termed transcranial alternating magnetic stimulation (tAMS), delivering in situ sinusoidal electric fields comparable to transcranial alternating current stimulation (tACS). Magnetophosphene perception was quantified in 81 volunteers exposed to MF (eye or occipital exposure) between 0 and 50 mT at frequencies of 20, 50, 60 and 100 Hz. Results: Reliable magnetophosphene perception was induced with tAMS without any scalp sensation, a major advantage as compared to tACS. Frequency-dependent thresholds were quantified using binary logistic regressions hence allowing to establish condition dependent probabilities of perception. Results support an interaction between induced current density and retinal rod cells. Conclusion: Beyond fundamental and immediate implications for international safety guidelines, and for identifying the interaction site underlying phosphene perception (ubiquitous in tACS experiments), our results support exploring the potential of tAMS for the differential diagnosis of retinal disorders and neuromodulation therapy.

Chunk memory constitutes the basic unit that manages long-term memory and converts it into immediate decision-making processes, it remains unclear how to interpret and organize incoming information to form effective chunk memory. This paper investigates electroencephalography (EEG) patterns from the perspective of time-domain feature extraction using chunk memory in visual statistical learning and combines time-resolved multivariate pattern analysis (MVPA). The GFP and MVPA results revealed that chunk memory processes occurred during specific time windows in the learning phase. These processes included attention modulation (P1), recognition and feature extraction (P2), and segmentation for long-term memory conversion (P6). In the decision-making stage, chunk memory processes were encoded by four ERP components. Scene processing correlated with P1, followed by feature extraction facilitated by P2, encoding process (P4), and segmentation process (P6). This paper identifies the early process of chunk memory through implicit learning and applies univariate and multivariate approaches to establish the neural activity patterns of the early chunk memory process, which provides ideas for subsequent related studies.

Seizures are neurological disorders triggered by an imbalance in the activity of excitatory and inhibitory neurotransmitters in the brain. When triggered chronically, this imbalance can lead to epilepsy. Critically, many of the affected individuals are refractory to treatment. Given this, anti-inflammatory drugs, in particular glucocorticoids, have been considered as a potential antiepileptogenic therapy. Glucocorticoids are currently used in the treatment of refractory patients, although there have been contradictory results in terms of their use in association with antiepileptic drugs, which reinforces the need for a more thorough investigation of their effects. In this context, the present study evaluated the effects of dexamethasone (DEX, 0.6 mg/kg) on the electroencephalographic (EEG) and histopathological parameters of male Wistar rats submitted to acute seizure induced by pentylenetetrazol (PTZ). The EEG monitoring revealed that DEX reduced the total brainwave power, in comparison with PTZ, in 12 h after the convulsive episode, exerting this effect in up to 36 h (p 0.05). While DEX did not reverse the cell damage caused by PTZ, the data indicate that DEX has beneficial properties in the EEG analysis, which makes it a promising candidate for the attenuation of the epileptiform wave patterns that can precipitate refractory seizures.

Empathy involves the processing of complex information related to dynamic interactions between the empathizer and target. One neural signature of empathy is the suppression of electroencephalographic mu rhythm (8–13 Hz) over the sensorimotor region. It is important to consider that few researchers have studied the effects of empathizer feedback on empathy and its underlying neural mechanism, and most previous research has lacked ecological validity due to standardized emotional stimuli and constraints on the experiment environment. Our objective in this study was to investigate the means by which empathizer feedback influences one's own empathy in naturalistic social situations. Our results revealed that empathizer feedback decreases empathic accuracy but does not affect the emotional contagion nor the emotional intensity of the empathizer. We also found that the ability to accurately infer sadness is hindered by empathizer feedback. Empathizers presented lower alpha activity in the sensorimotor cortical areas only while receiving sad narratives and not providing feedback. This study contributes to the emerging research on the influence of empathizer feedback in naturalistic social settings.

It is known that Temporal Information Processing (TIP) underpins our cognitive functioning. Previous research has focused on the relationship between TIP efficiency and oscillatory brain activity, especially the gamma rhythm; however, non-oscillatory (aperiodic or 1/f) brain activity has often been missed. Recent studies have identified the 1/f component as being important for the functioning of the brain. Therefore, the current study aimed to verify whether TIP efficiency is associated with specific EEG resting state cortical activity patterns, including oscillatory and non-oscillatory (aperiodic) brain activities. To measure individual TIP efficiency, we used two behavioral tasks in which the participant judges the order of two sounds separated by millisecond intervals. Based on the above procedure, participants were classified into two groups with high and low TIP efficiency. Using cluster-based permutation analyses, we examined between-group differences in oscillatory and non-oscillatory (aperiodic) components across the 1–90 Hz range. The results revealed that the groups differed in the aperiodic component across the 30–80 Hz range in fronto-central topography. In other words, participants with low TIP efficiency exhibited higher levels of aperiodic activity, and thus a flatter frequency spectrum compared to those with high TIP efficiency. We conclude that participants with low TIP efficiency display higher levels of ‘neural noise’, which is associated with poorer quality and speed of neural processing.

Background: The limited understanding of the physiology and psychology of polar expedition explorers has prompted concern over the potential cognitive impairments caused by exposure to extreme environmental conditions. Prior research has demonstrated that such stressors can negatively impact cognitive function, sleep quality, and behavioral outcomes. Nevertheless, the impact of the polar environment on neuronal activity remains largely unknown. Methods: In this study, we aimed to investigate spatiotemporal alterations in brain oscillations of 13 individuals (age range: 22–48 years) who participated in an Arctic expedition. We utilized electroencephalography (EEG) to record cortical activity before and during the Arctic journey, and employed standardized low resolution brain electromagnetic tomography to localize changes in alpha, beta, theta, and gamma activity. Results: Our results reveal a significant increase in the power of theta oscillations in specific regions of the Arctic, which differed significantly from pre-expedition measurements. Furthermore, microstate analysis demonstrated a significant reduction in the duration of microstates (MS) D and alterations in the local synchrony of the frontoparietal network. Conclusion: Overall, these findings provide novel insights into the neural mechanisms underlying adaptation to extreme environments. These findings have implications for understanding the cognitive consequences of polar exploration and may inform strategies to mitigate potential neurological risks associated with such endeavors. Further research is warranted to elucidate the long-term effects of Arctic exposure on brain function.

Electroencephalography (EEG) is a non-intrusive and powerful tool for understanding the complex nature of the brain. Epileptic seizures, a neurological disorder, are characterized by excessively synchronized neuronal activity within the brain. This abnormal synchronization disrupts the normal functioning of neural networks, leading to seizures. This work proposes to use the Gaussian-based Stockwell Transform (GST) to segment EEG signals into various frequency bands such as delta, theta, alpha, beta, and gamma band for analysis. The feature extraction process on these sub-band signals generates a feature set, encompassing the fluctuation index, relative amplitude, holoentropy, logarithmic band power, and the newly introduced Hermite polynomial-based delta amplitude modulation spectrogram. To differentiate between epileptic and non-epileptic events in EEG signals, the previous features have been applied to the Random Forest Classifier (RFC) algorithm. The effectiveness of our proposed method is validated using a benchmark EEG dataset, with GST and RFC demonstrating accurate results in distinguishing seizure activity. Experimental results show an optimal classification accuracy of 96.4 %, a sensitivity of 97 %, and a specificity of 96.4 %.

Objectives: To elucidate the functional characteristics of the brain in the presence of chronic pain using electroencephalography (EEG), with a focus on the dynamics of neural excitation and inhibition. Methods: Resting-state EEG was performed in: 17 patients with complex regional pain syndrome (CRPS) who exhibited chronic pain higher than 20 on the visual analogue scale (VAS), 6 patients with reduced CRPS symptoms and chronic pain less than 20 on VAS, and healthy age-matched controls. For the analysis, 50 s of electroencephalogram (EEG) signals were extracted from EEG recordings during wakefulness and rest with eyes closed. The envelope of the alpha frequency band was calculated by examining the positive and negative accelerations of the envelope oscillation, ratio of positive (Ap) to negative (An) accelerations (Ap-An ratio), and mean amplitude of the envelope. Comparisons were made between patients and controls, and correlations between these EEG measures and the subjective pain VAS were evaluated.Significant differences in the value of Ap, An and Ap-An ratio were observed at temporal and central electrodes between patients with pain symptoms and controls. Those with reduced CRPS symptoms exhibited a distinct Ap-An ratio at the majority of electrodes when compared with those exhibiting chronic pain. Conclusions: Distinct patterns in alpha wave envelope dynamics, reflecting excitatory and inhibitory activities, were associated with chronic pain in patients with CRPS. The pain-relieved state of CRPS suggested that a new balance of activities was established. This relationship indicated a potential association between altered alpha oscillation characteristics and the subjective experience of pain. Significance: This study introduces a novel method for analyzing alpha oscillation envelopes, providing new insights into the neural pathophysiology of chronic pain in CRPS patients. This approach has the potential to enhance our understanding of the alterations in brain function that occur under chronic pain conditions.

Summary: In an era of ‘big data’, we propose that a collaborative network approach will drive a better understanding of the mechanisms of delirium, and more rapid development of therapies. We have formed the International Delirium Pathophysiology & Electrophysiology Network for Data sharing (iDEPEND) group with a key aim to ‘facilitate the study of delirium pathogenesis with electrophysiology, imaging, and biomarkers including data acquisition, analysis, and interpretation’. Our initial focus is on studies of electrophysiology as we anticipate this methodology has great potential to enhance our understanding of delirium. Our article describes this principle and is used to highlight the endeavour to the wider community as we establish key stakeholders and partnerships.

Time Delay Stability (TDS) is an established tool for analyzing the interaction between physiological systems in the human organism. Time series are measured with sensors from different organ systems and are analyzed pairwise. Each pair is characterized by a TDS link strength and by combining these to a network, insights into underlying physiological mechanisms can be obtained. Computing TDS is based on heuristic computations with multiple open parameters. In the past, research groups working with TDS have implemented their own algorithms in different programming languages, which posed the risk of differences between implementations and parameters, leading to a lack of reproducibility. Therefore, we propose a reference implementation written in Python 3, entitled TDSpython (TDSpy) that we make publicly available via the Python Package Index (PyPI). In this paper, we give a comprehensive description of the implementation, demonstrate its usage on publicly-available sleep research data, and evaluate its suitability by reproducing published studies. In addition, we apply TDSpy to data from comatose patients, emphasizing its generalizability.

In electroencephalographic (EEG) data, power-frequency slope exponents (1/f_β) can provide non-invasive markers of in vivo neural activity excitation-inhibition (E:I) balance. E:I balance may be altered in neurodevelopmental conditions; hence, understanding how 1/f β evolves across infancy/childhood has implications for developing early assessments/interventions. This systematic review (PROSPERO-ID: CRD42023363294) explored the early maturation (0–26 yrs) of resting-state EEG 1/f measures (aperiodic [AE], power law [PLE] and Hurst [HE] exponents), including studies containing ≥1 1/f measures and ≥10 typically developing participants. Five databases (including Embase and Scopus) were searched during March 2023. Forty-two studies were identified (Nparticipants=3478). Risk of bias was assessed using the Quality Assessment with Diverse Studies tool. Narrative synthesis of HE data suggests non-stationary EEG activity occurs throughout development. Age-related trends were complex, with rapid decreases in AEs during infancy and heterogenous changes thereafter. Regionally, AE maxima shifted developmentally, potentially reflecting spatial trends in maturing brain connectivity. This work highlights the importance of further characterising the development of 1/f measures to better understand how E:I balance shapes brain and cognitive development.

Although one can recognize the environment by soundscape substituting vision to auditory signal, whether subjects could perceive the soundscape as visual or visual-like sensation has been questioned. In this study, we investigated hierarchical process to elucidate the recruitment mechanism of visual areas by soundscape stimuli in blindfolded subjects. Twenty-two healthy subjects were repeatedly trained to recognize soundscape stimuli converted by visual shape information of letters. An effective connectivity method called dynamic causal modeling (DCM) was employed to reveal how the brain was hierarchically organized to recognize soundscape stimuli. The visual mental imagery model generated cortical source signals of five regions of interest better than auditory bottom-up, cross-modal perception, and mixed models. Spectral couplings between brain areas in the visual mental imagery model were analyzed. While within-frequency coupling is apparent in bottom-up processing where sensory information is transmitted, cross-frequency coupling is prominent in top-down processing, corresponding to the expectation and interpretation of information. Sensory substitution in the brain of blindfolded subjects derived visual mental imagery by combining bottom-up and top-down processing.

Motor and physical fitness play a crucial role to optimize the player performance in the soccer game. Implementing a customized workout schedule targeting Motophysic Fitness (MPF) helps to elevate their on-field performance. The present study was aimed to assess the impact of a 12-week MPF Training program targeted to have improvements in physical fitness for elite soccer players. MPF training program was implemented during the off-season for youth soccer players with average age (20 ± 1 years), height (1.75 ± 0.5 m) weight (64.3 ± 5.7 kg). Soccer-related fitness traits were assessed to evaluate performance levels, while Electroencephalography (EEG) was utilized for cognitive assessment. Statistical analysis was performed to evaluate the outcome of the fitness traits. Significant improvements were observed across diverse fitness traits, with mean values increasing by a minimum of 2 % and a maximum of 5 %. Speed emerged as a predominant contributor, showing a robust correlation (Adjusted R2 = 0.84), while agility, strength, power, endurance, balance, coordination, and reaction time also displayed substantial improvements. Despite observable gains in flexibility, its impact on overall fitness appeared comparatively modest (Adjusted R2 = 0.23). Z-test confirmed the statistical significance of all fitness assessments post-MPF, with p-values less than 0.05 for each test. Features extracted from EEG highlighted the improvement in cognitive ability before and after training.

Sleep fragmentation (SF) is a common sleep problem experienced during the perioperative period by older adults, and is associated with postoperative cognitive dysfunction (POCD). Increasing evidence indicates that delta-wave activity during non-rapid eye movement (NREM) sleep is involved in sleep-dependent memory consolidation and that hippocampal theta oscillations are related to spatial exploratory memory. Recovery sleep (RS), a self-regulated state of sleep homeostasis, enhances delta-wave power and memory performance in sleep-deprived older mice. However, it remains unclear whether RS therapy has a positive effect on cognitive changes following SF in older mouse models. Therefore, this study aimed to explore whether preoperative RS can alleviate cognitive deficits in aged mice with SF. A model of preoperative 24-h SF combined with exploratory laparotomy-induced POCD was established in 18-month-old mice. Aged mice were treated with preoperative 6-h RS following SF and postoperative 6-h RS following surgery, respectively. The changes in hippocampus-dependent cognitive function were investigated using behavioral tests, electroencephalography (EEG), local field potential (LFP), magnetic resonance imaging, and neuromorphology. Mice that underwent 24-h SF combined with surgery exhibited severe spatial memory impairment; impaired cognitive performance could be alleviated by preoperative RS treatment. In addition, preoperative RS increased NREM sleep; enhanced EEG delta-wave activity and LFP theta oscillation in the hippocampal CA1; and improved hippocampal perfusion, microstructural integrity, and neuronal damage. Taken together, these results provide evidence that preoperative RS may ameliorate the severity of POCD aggravated by SF by enhancing delta slow-wave activity and hippocampal theta oscillation, and by ameliorating the reduction in regional cerebral blood flow and white matter microstructure integrity in the hippocampus.

Despite improvements in cardiopulmonary resuscitation (CPR), survival and neurologic recovery after cardiac arrest remain poor due to ischemia and subsequent reperfusion injury. As the likelihood of survival and favorable neurologic outcome decreases with increasing severity of ischemia during CPR, developing methods to measure the magnitude of ischemia during resuscitation is critical for improving overall outcomes. Cerebral oximetry, which measures regional cerebral oxygen saturation (rSO2) by near‐infrared spectroscopy, has emerged as a potentially beneficial marker of cerebral ischemia during CPR. In numerous preclinical and clinical studies, higher rSO2 during CPR has been associated with improved cardiac arrest survival and neurologic outcome. There is also emerging evidence that this can be integrated with electroencephalogram (EEG) monitoring to provide a bimodal system of brain monitoring during CPR. In this method’s review, we discuss the feasibility, application, and implications of this integrated monitoring approach, highlighting its significance for improving clinical outcomes in cardiac arrest management and guiding future research directions.

This study investigated the savory intensity of aroma-active compounds derived from yeast extract Maillard reaction models. Sensory evaluation results revealed that beef flavoring model (28.00 g) exhibited the highest savory perception intensity when the yeast extract FA34 (0.50 g) the added. Eleven aroma-active compounds associated with saltiness perception were identified via solid-phase microextraction and extraction combined with gas chromatography–mass spectrometry/olfactory. The odorant–NaCl mixture model and saltiness intensity evaluation results revealed that thiazole and 4-methylpentanoic acid could significantly (p < 0.05) enhance the saltiness perception of salt solution (5.00 g/L), 2-methylpyrazine, 2-methyl-3-furanthiol, 2,6-dimethylpyrazine, furfuryl mercaptan, and methyl 2-methyl-3-furyl disulfide could significantly (p < 0.01) enhance the saltiness perception of a salt solution (5.00 g/L). Electroencephalography revealed that the main mechanisms underlying aroma-induced saltiness perception enhancement included the strengthening of the saltiness perception signal and prolonging signal stimulation time in the frontal regions of the cerebral cortex.

Electroencephalography (EEG) is an important tool in the field of developmental cognitive neuroscience for indexing neural activity. However, racial biases persist in EEG research that limit the utility of this tool. One bias comes from the structure of EEG nets/caps that do not facilitate equitable data collection across hair textures and types. Recent efforts have improved EEG net/cap design, but these solutions can be time-intensive, reduce sensor density, and are more difficult to implement in younger populations. The present study focused on testing EEG sensor net designs over infancy. Specifically, we compared EEG data quality and retention between two high-density saline-based EEG sensor net designs from the same company (Magstim EGI, Whitland, UK) within the same infants during a baseline EEG paradigm. We found that within infants, the tall sensor nets resulted in lower impedances during collection, including lower impedances in the key online reference electrode for those with greater hair heights and resulted in a greater number of usable EEG channels and data segments retained during pre-processing. These results suggest that along with other best practices, the modified tall sensor net design is useful for improving data quality and retention in infant participants with curly or tightly-coiled hair.

The field of developmental cognitive neuroscience is advancing rapidly, with large-scale, population-wide, longitudinal studies emerging as a key means of unraveling the complexity of the developing brain and cognitive processes in children. While numerous neuroscientific techniques like functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) have proved advantageous in such investigations, this perspective proposes a renewed focus on electroencephalography (EEG), leveraging underexplored possibilities of EEG. In addition to its temporal precision, low costs, and ease of application, EEG distinguishes itself with its ability to capture neural activity linked to social interactions in increasingly ecologically valid settings. Specifically, EEG can be measured during social interactions in the lab, hyperscanning can be used to study brain activity in two (or more) people simultaneously, and mobile EEG can be used to measure brain activity in real-life settings. This perspective paper summarizes research in these three areas, making a persuasive argument for the renewed inclusion of EEG into the toolkit of developmental cognitive and social neuroscientists.

The theta band is one of the most prominent frequency bands in the electroencephalography (EEG) power spectrum and presents an interesting paradox: while elevated theta power during resting state is linked to lower cognitive abilities in children and adolescents, increased theta power during cognitive tasks is associated with higher cognitive performance. Why does theta power, measured during resting state versus cognitive tasks, show differential correlations with cognitive functioning? This review provides an integrated account of the functional correlates of theta across different contexts. We first present evidence that higher theta power during resting state is correlated with lower executive functioning, attentional abilities, language skills, and IQ. Next, we review research showing that theta power increases during memory, attention, and cognitive control, and that higher theta power during these processes is correlated with better performance. Finally, we discuss potential explanations for the differential correlations between resting/task-related theta and cognitive functioning, and offer suggestions for future research in this area.

Background: The alpha-2 adrenergic agonist dexmedetomidine induces EEG patterns resembling those of non-rapid eye movement (NREM) sleep. Fulfilment of slow wave sleep (SWS) homeostatic needs would address the assumption that dexmedetomidine induces functional biomimetic sleep states. Methods: In-home sleep EEG recordings were obtained from 13 healthy participants before and after dexmedetomidine sedation. Dexmedetomidine target-controlled infusions and closed-loop acoustic stimulation were implemented to induce and enhance EEG slow waves, respectively. EEG recordings during sedation and sleep were staged using modified American Academy of Sleep Medicine criteria. Slow wave activity (EEG power from 0.5 to 4 Hz) was computed for NREM stage 2 (N2) and NREM stage 3 (N3/SWS) epochs, with the aggregate partitioned into quintiles by time. The first slow wave activity quintile served as a surrogate for slow wave pressure, and the difference between the first and fifth quintiles as a measure of slow wave pressure dissipation. Results: Compared with pre-sedation sleep, post-sedation sleep showed reduced N3 duration (mean difference of −17.1 min, 95% confidence interval −30.0 to −8.2, P=0.015). Dissipation of slow wave pressure was reduced (P=0.02). Changes in combined durations of N2 and N3 between pre- and post-sedation sleep correlated with total dexmedetomidine dose, (r=−0.61, P=0.03). Conclusions: Daytime dexmedetomidine sedation and closed-loop acoustic stimulation targeting EEG slow waves reduced N3/SWS duration and measures of slow wave pressure dissipation on the post-sedation night in healthy young adults. Thus, the paired intervention induces sleep-like states that fulfil certain homeostatic NREM sleep needs in healthy young adults. Clinical trial registration: ClinicalTrials.gov NCT04206059.

Chronic pelvic pain (CPP) is a debilitating problem in women with clear evidence of myofascial dysfunction. It seems that Myofascial trigger points (MTrPs) contribute to the development of central sensitization (CS). This study aimed to investigate the effect of dry needling on pain and CS in women with CPP. Thirty-six women with CPP participated in this randomized controlled clinical trial and randomly assigned into three groups: dry needling group (DNG), placebo needling group (PNG) and control group (CG). The DNG received five sessions of DN using the “static needling”, the PNG received non-penetrating method, and the CG did not receive any intervention. Assessment of outcomes including central sensitization inventory (CSI), short-form McGill pain questionnaire (SF-MPQ), electroencephalography (EEG), conditioned pain modulation (CPM), salivary cortisol concentration, 7-item general anxiety disorder scale (GAD-7), pain catastrophizing scale (PCS), and SF-36 questionnaire was performed pre-intervention, post-intervention, and three months post-intervention by a blind examiner. The result showed a significant group-by-time interaction for CSI, SF-MPQ, and PCS. There was a significant decrease in CSI score in post-intervention and three-months post-intervention compare to pre-intervention in the DNG and PNG. SF-MPQ-PPI score in DNG significantly decreased post-intervention. PCS-Total score decreased significantly post-intervention in DNG and PNG. No significant group-by-time interactions were observed for other variables. EEG results showed regional changes in the activity of frequency bands in both eye closed and eye open conditions. It seems that DN can affect central pain processing by removing the source of peripheral nociception.Trial registration: Iranian Registry of Clinical Trials (IRCT20211114053057N1, registered on: December 03, 2021. https://irct.behdasht.gov.ir/search/result?query=IRCT20211114053057N1).

Aim: To compare the safety and efficacy of intranasal high-dose dexmedetomidine (DEX) versus a combination of intranasal low-dose dexmedetomidine and oral chloral hydrate (DEX-CH) sedation during electroencephalography (EEG) in children. Methods: Unadjusted analysis, 1:1 propensity score matching (PSM), and inverse probability of treatment weighting (IPTW) were used to compare the sedation success rate, adverse effects, onset time, and recovery time of these two sedation methods for 6967 children who underwent EEG. Results: A total of 6967 children were enrolled in this study, of whom 846 (12.1 %) underwent DEX intranasal sedation while 6121 (87.9 %) received DEX-CH sedation. No significant differences were observed in the sedation success rate with the first dose between the two groups [824 (97.4 %) for DEX vs. 5971 (97.6 %) for DEX-CH; RR 0.99; 95 % CI, 0.98–1.01; P = 0.79]. Similarly, there were no notable disparities in the incidence of adverse events [16 (1.9 %) for DEX vs. 101 (1.7 %) for DEX-CH; RR 1.15; 95 % CI, 0.68–1.93; P = 0.32]. However, intranasal DEX sedation compared with DEX-CH sedation was associated with lower vomiting [0 vs. 95(1.6 %); RR 0.04; 95 % CI, 0.02–0.6; P = 0.02] or more bradycardia [13(1.5 %) vs. 2(0.03 %); RR 47.03; 95 % CI, 10.63–208.04; P

Fragile X Syndrome (FXS) is a leading known genetic cause of intellectual disability with symptoms that include increased anxiety and social and sensory processing deficits. Recent electroencephalographic (EEG) studies in humans with FXS have identified neural oscillation deficits that include increased resting state gamma power, increased amplitude of auditory evoked potentials, and reduced phase locking of sound-evoked gamma oscillations. Similar EEG phenotypes are present in mouse models of FXS, but very little is known about the development of such abnormal responses. In the current study, we employed a 30-channel mouse multielectrode array (MEA) system to record and analyze resting and stimulus-evoked EEG signals in male P21 and P91 WT and Fmr1 KO mice. This led to several novel findings. First, P91, but not P21, Fmr1 KO mice have significantly increased resting EEG power in the low- and high-gamma frequency bands. Second, both P21 and P91 Fmr1 KO mice have markedly attenuated inter-trial phase coherence (ITPC) to spectrotemporally dynamic auditory stimuli as well as to 40 Hz and 80 Hz auditory steady-state response (ASSR) stimuli. This suggests abnormal temporal processing from early development that may lead to abnormal speech and language function in FXS. Third, we found hemispheric asymmetry of fast temporal processing in the mouse auditory cortex in WT but not Fmr1 KO mice. Together, these findings define a set of EEG phenotypes in young and adult mice that can serve as translational targets for genetic and pharmacological manipulation in phenotypic rescue studies.

Emotion recognition technology through EEG signal analysis is currently a fundamental concept in artificial intelligence. This recognition has major practical implications in emotional health care, human-computer interaction, and so on. This paper provides a comprehensive study of different methods for extracting electroencephalography (EEG) features for emotion recognition from four different perspectives, including time domain features, frequency domain features, time-frequency features, and nonlinear features. We summarize the current pattern recognition methods adopted in most related works, and with the rapid development of deep learning (DL) attracting the attention of researchers in this field, we pay more attention to deep learning-based studies and analyse the characteristics, advantages, disadvantages, and applicable scenarios. Finally, the current challenges and future development directions in this field were summarized. This paper can help novice researchers in this field gain a systematic understanding of the current status of emotion recognition research based on EEG signals and provide ideas for subsequent related research.

Background: The analysis and interpretation of transcranial magnetic stimulation (TMS)-evoked potentials (TEPs) relies on successful cleaning of the artifacts, which typically mask the early (0–30 ms) TEPs. Independent component analysis (ICA) is possibly the single most utilized methodology to clean these signals. Objective: ICA-based cleaning is reliable provided that the input data are composed of independent components. Differently, in case the underlying components are to some extent dependent, ICA algorithms may yield erroneous estimates of the components, resulting in incorrectly cleaned data. We aim to ascertain whether TEP signals are suited for ICA. Methods: We present a systematic analysis of how the properties of simulated artifacts imposed on measured artifact-free TEPs affect the ICA results. The variability of the artifact waveform over the recorded trials is varied from deterministic to stochastic. We measure the accuracy of ICA-based cleaning for each level of variability. Results: Our findings indicate that, when the trial-to-trial variability of an artifact component is small, which can result in dependencies between underlying components, ICA-based cleaning biases towards eliminating also non-artifactual TEP data. We also show that the variability can be measured using the ICA-derived components, which in turn allows us to estimate the cleaning accuracy. Conclusion: As TEP artifacts tend to have small trial-to-trial variability, one should be aware of the possibility of eliminating brain-derived EEG when applying ICA-based cleaning strategies. In practice, after ICA, the artifact component variability can be measured, and it predicts to some extent the cleaning reliability, even when not knowing the clean ground-truth data.

Though a common symptom, fatigue is difficult to define and investigate, occurs in a wide variety of neurological and systemic disorders, with differing pathological causes. It is also often accompanied by a psychological component. As a symptom of long-term COVID-19 it has gained more attention.In this review, we begin by differentiating fatigue, a perception, from fatigability, quantifiable through biomarkers. Central and peripheral nervous system and muscle disorders associated with these are summarised. We provide a comprehensive and objective framework to help identify potential causes of fatigue and fatigability in a given disease condition. It also considers the effectiveness of neurophysiological tests as objective biomarkers for its assessment. Among these, twitch interpolation, motor cortex stimulation, electroencephalography and magnetencephalography, and readiness potentials will be described for the assessment of central fatigability, and surface and needle electromyography (EMG), single fibre EMG and nerve conduction studies for the assessment of peripheral fatigability.The purpose of this review is to guide clinicians in how to approach fatigue, and fatigability, and to suggest that neurophysiological tests may allow an understanding of their origin and interactions. In this way, their differing types and origins, and hence their possible differing treatments, may also be defined more clearly.

Autonomous Sensory Meridian Response (ASMR) is an audio-visual phenomenon that has recently become popular. Many people have reported experiencing a tingling-like sensation through their body while watching audio/video clips known as ASMR clips. People capable of having such experiences have also reported improved overall well-being and feeling relaxed. However, the neural activity underlying this phenomenon is not yet well-studied. The present study aims to investigate this issue using electroencephalography (EEG) employing an exploratory approach. We recorded resting-state EEGs from twelve participants before and after watching an ASMR clip and a control video clip. We divided the participants into two groups capable of experiencing ASMR tingling (ASMR group) and not capable of experiencing ASMR tingling (Non-ASMR group), by performing “Jenks Natural Breaks” clustering method on the results of a self-report questionnaire. We calculated the spectral power of EEG recording and compared the resulting values between the groups and sessions. We demonstrated a decline in the power of EEG activities in the delta frequency band in all regions of the brain and an increase in alpha activity in the occipital area of the brain and increases in beta oscillations was noted over the left fronto-temporal region of the brain among ASMR group. We did not observe similar results among the Non-ASMRs participants or among ASMRs in the control group.

The objectives of this study were (1) to develop a prototype for controlling the movements of a robot by using green electroencephalography (EEG) energy and (2) to test a prototype algorithm’s effectiveness for controlling robot movements. The study was conducted as experimental research. An EEG detects electrical energy from activities of the brain and nervous system. The brain’s electrical energy or EEG is produced by thinking or the mind controlling, blinking, meditating, and focusing. The raw acquired EEG data underwent preprocessing and feature extraction. The acquired EEG signals were used to control the robot. Algorithms were designed and developed to connect the EEG data and the robot. The effectiveness of the prototype algorithm in controlling a robot to perform unidirectional movement was determined. The algorithm achieved an accuracy of 81.43%. Likewise, an evaluation of the effect of the prototype algorithm in controlling a robot to perform continuous movement was performed. The algorithm was modified and combined with a gyroscope accelerometer magnetic sensor (IMU) node in a hybrid manner. The experimental result presented 80.22% accuracy, and the algorithm worked properly and consumed 2.40 min of time. The results indicated that the EEG algorithm could correctly control robot movements.

The present database contains brain activity of subjective tinnitus sufferers at identifying their sound tinnitus. The main objective of this database is to provide spontaneous Electroencephalographic (EEG) activity at rest, and evoked EEG activity when tinnitus sufferers attempt to identify their sound tinnitus among 54 tinnitus sound examples. For the database, 37 volunteers were recruited: 15 ones without tinnitus (Control Group – CG), and 22 ones with tinnitus (Tinnitus Group – TG). For EEG recording, 30 channels were used to record two conditions: 1) basal condition, where the volunteer remained in a state of rest with the open eyes for two minutes; and 2) active condition, where the volunteer must have identified his/her sound stimulus by pressing a key. For the active condition, a sound-tinnitus library was generated in accordance with the most typical acoustic properties of tinnitus. The library consisted in ten pure tones (250 Hz, 500 Hz, 1 kHz, 2 kHz, 3 kHz, 3.5 kHz, 4 kHz, 6 kHz, 8 kHz, 10 kHz), a White Noise (WN), a Narrow Band noise-High frequencies (NBH, 4 kHz–10 kHz), a Narrow Band noise-Medium frequencies (NBM,1 kHz–4 kHz), a Narrow-Band noise Low frequencies (NBL, 250 Hz–1 kHz), ten pure tones combined with WN, ten pure tones superimposed with NBH, ten tones with NBM and ten pure tones combined with NBL. In total, 54 sound-tinnitus were applied for both groups. In the case of CG, volunteers must have identified a sound at 3.5 kHz. In addition to EEG information, a csv-file with audiometric and psychoacoustic information of volunteers is provided. For TG, this information refers to: 1) hearing level, 2) type of tinnitus, 3) tinnitus frequency, 4) tinnitus perception, 5) Hospital Anxiety and Depression Scale (HADS) and 6) Tinnitus Functional Index (TFI). For CG, the information refers to: 1) hearing level, and 2) HADS.

This article described a publicly available dataset of the visual cognitive motivation study in healthy adults. To gain an in-depth understanding and insights into motivation, Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were measured simultaneously at shared locations while participants performed a visual cognitive motivation task. The participants’ choices in the cognitive motivation task were recorded. The effects of their motivation were identified in the recognition test afterward. This dataset comprised EEG and fNIRS data from sixteen healthy adults (age: 21- 37 years; 14 males and 2 females) during the cognitive motivation task with visual scenic stimuli. In addition, the motivation and the corresponding motivation effect were also provided. This dataset provides understanding and analyzing opportunities for the process of attention and decision while the brain undergoes an induced motivated state and its effect on the recognition performance.

Background: Cavernous malformation (CM) is a well-known cause of epilepsy. Although the location of the CM is usually consistent with the side of seizure onset, some reports have described discrepancies between results from scalp electroencephalography (EEG) and CM location. This study investigated the prevalence and features of patients showing false lateralization (FL). Particularly, we tested the hypothesis that patients showing FL were more likely to have CM in medial and deep areas of the brain than in other areas. Methods: Patients diagnosed with CM-associated epilepsy in our institution between March 2009 and March 2023 were included in this retrospective analysis. We investigated the presence or absence of FL of interictal epileptiform discharges (IEDs) or ictal discharges against MRI findings or against the true focus as determined from surgical outcomes. We compared the FL group with the non-false-lateralization group (NFL group) to clarify features of CM-associated epilepsy patients showing FL. Results: Thirty-two epilepsy patients with CM were analyzed. The frequency of FL to MRI was 10.3% for IEDs and 7.7% for ictal discharges, while the frequency of FL to true focus after removal surgery was 10.5% for IEDs and 7.7% for ictal discharges. Regarding the FL of IEDs against MRI findings, the percentage of medial and deep lesions was significantly higher in the FL group (3/3, 100%) than in the NFL group (6/26, 23.1%; p = 0.023). No significant differences in age, sex, seizure type, or size of the CM were seen between groups. Conclusions: CM-associated epilepsy can also present with FL, particularly if the location of the CM is medial and deep. Caution may be needed in determining the area for resection based solely on scalp EEG findings.

Background: Personalization is considered an important principle in virtual reality (VR) exposure therapy. We aimed to identify whether personalized VR exposure could provoke increased anxiety in patients with panic disorder and agoraphobia as it is considered the first step in successful treatment for anxiety. Methods: We performed a double-arm, one-day preliminary study among 28 patients with panic disorder and agoraphobia. Three sessions of VR exposure, including a theater, train, and elevator scenario, were conducted in two groups. In the personalized group (n = 14), the brightness and crowd density were customized based on a pre-assessment. In the control group (n = 14), these conditions were fully randomized. Self-reported anxiety, heart rate, skin conductance, and electroencephalography were measured before, during, and after the VR sessions. Results: In the later VR sessions, higher self-reported anxiety levels measured by the Visual Analogue Scale were observed in the personalized exposure group. Increased heart rates during and after the VR sessions were observed in the personalized group. The changes in skin conductance peaks were not significantly different between the groups, but the increase in skin conductance was associated with the participants' perception of presence. The electroencephalogram showed widespread increases in alpha waves in the frontal and temporal areas of the brain in the personalized group than in the control group. Conclusion: Personalized VR exposure elicits stronger anxiogenic effects in patients with panic disorder and agoraphobia as suggested by self-report and neurophysiological data. Personalization of VR exposure has the potential for effective behavioral therapy.

User behavior plays a substantial role in shaping household energy use. Nevertheless, the methodologies employed by researchers to examine user behavior exhibit certain limitations in terms of their reach. The present article introduces an openly accessible collection of electroencephalography (EEG) recordings, comprising EEG data collected from individuals who were subjected to energy data visualizations. The dataset comprises EEG recordings obtained from 28 individuals who were in good health. The EEG recordings were collected using a 32-channel EMOTIV EEG device, and the international 10-20 electrode system was employed for precise electrode placement. The energy data visualizations were generated and showcased utilizing the PsychoPy software. To ascertain the participants' affective state, they were requested to rate the valence and arousal of each stimulus through the utilization of a self-assessment manikin (SAM). Additionally, three inquiries were posed for every stimulation. The dataset includes both original data visualizations and ratings. Additionally, the raw EEG data has been divided into segments consisting of data visualizations and neutral images, with the use of event markers, in order to assist analysis. The EEG recordings were recorded and stored utilizing the EMOTIVPro application, whereas the subjective reactions were captured and preserved using the PsychoPy application. Furthermore, the generation of synthetic EEG data is accomplished by employing the Generative Adversarial Network (GAN) architecture on the acquired EEG dataset. The synthetic EEG data created is integrated with empirical EEG data, and afterwards subjected to qualitative and quantitative analysis in order to improve performance. The dataset presented herein showcases a pioneering utilization of EEG investigation and offers a valuable foundation for scholars in the domains of computer science, energy conservation, artificial intelligence, brain-computer interfaces, and human-computer interaction.