BACKGROUND: Previous studies have reported that the sense of \"self\" is associated with specific brain regions and neural network activities. In addition, the mirror system, which functions when executing or observing an action, might contribute to differentiating the self from others and form the basis of the sense of self as a fundamental physical representation. This study investigated whether differences in mu suppression, an indicator of mirror system activity, reflect cognitions related to self-other discrimination.
METHODS: The participants were 30 of healthy college students. The participants observed short video clips of hand movements performed by themselves or actors from two perspectives (i.e., first-person and third-person). The electroencephalogram (EEG) mu rhythm (8-13 Hz) was measured during video observation as an index of mirror neuron system activity. EEG activity related to self-detection was analyzed using participants\' hand movements as self-relevant stimuli.
RESULTS: The results showed that mu suppression in the 8-13-Hz range exhibited perspective-dependent responses to self/other stimuli. There was a significant self-oriented mu suppression response in the first-person perspective. However, the study found no significant response orientation in the third-person perspective. The results suggest that mirror system activity may involve self-other discrimination differently depending on the perspective.
CONCLUSIONS: In summary, this study examined the mirror system\'s activity for self and others using the EEG\'s mu suppression. As a result, it was suggested that differences in self and others or perspectives may influence mu suppression.

UNASSIGNED: Currents in the brain flow inside neurons and across their boundaries into the extracellular medium, create electric and magnetic fields. These fields, which contain suitable information on brain activity, can be measured by electroencephalography (EEG), magnetoencephalography (MEG), and direct neural imaging.
UNASSIGNED: In this paper, we employed an electromagnetic model of the neuron activity and human head to derive electric and magnetic fields (brain waves) using a full-wave approach (ie. without any approximation). Currently, the brain waves are only derived using the quasi-static approximation (QSA) of Maxwell\'s equations in electromagnetic theory.
UNASSIGNED: As a result, source localization in brain imaging will produce some errors. So far, the error rate of the QSA on the output results of electric and magnetic fields has not been investigated. This issue has become more noticeable due to the increased sensitivity of modern electroencephalography (EEG) and magnetoencephalography (MEG) devices. This work introduces issues that QSA encounters in this problem and reveals the necessity of a full-wave solution. Then, a full-wave solution of the problem in closed-form format is presented for the first time. This solution is done in two scenarios: the source (active neurons) is in the center of a sphere, and when the source is out of the center but deeply inside the sphere. The first scenario is simpler, but the second one is much more complicated and is solved using a partial-wave series expression.
UNASSIGNED: One of the significant achievements of this model is improving the interpretation of EEG and MEG measurements, resulting in more accurate source localization.

Recently, there has been a resurgence in experimental and conceptual efforts to understand how brain rhythms can serve to organize visual information. Oscillations can provide temporal structure for neuronal processing and form a basis for integrating information across brain areas. Here, we use a bistable paradigm and a data-driven approach to test the hypothesis that oscillatory modulations associate with the integration or segregation of visual elements. Spectral signatures of perception of bound and unbound configurations of visual moving stimuli were studied using magnetoencephalography (MEG) in ambiguous and unambiguous conditions. Using a 2 × 2 design, we were able to isolate correlates from visual integration, either perceptual or stimulus-driven, from attentional and ambiguity-related activity. Two frequency bands were found to be modulated by visual integration: an alpha/beta frequency and a higher frequency gamma-band. Alpha/beta power was increased in several early visual cortical and dorsal visual areas during visual integration, while gamma-band power was surprisingly increased in the extrastriate visual cortex during segregation. This points to an integrative role for alpha/beta activity, likely from top-down signals maintaining a single visual representation. On the other hand, when more representations have to be processed in parallel gamma-band activity is increased, which is at odds with the notion that gamma oscillations are related to perceptual coherence. These modulations were confirmed in intracranial EEG recordings and partially originate from distinct brain areas. Our MEG and stereo-EEG data confirms predictions of binding mechanisms depending on low-frequency activity for long-range integration and for organizing visual processing while refuting a straightforward correlation between gamma-activity and perceptual binding. PRACTITIONER POINTS: Distinct neurophysiological signals underlie competing bistable percepts. Increased alpha/beta activity correlate with visual integration while gamma correlates with segmentation. Ambiguous percepts drive alpha/beta activity in the posterior cingulate cortex.

The global recognition of the importance of physical exercise (PE) for human health has resulted in increased research on its effects on cortical activity. Neural oscillations, which are prominent features of brain activity, serve as crucial indicators for studying the effects of PE on brain function. Existing studies support the idea that PE modifies various types of neural oscillations. While EEG-related literature in exercise science exists, a comprehensive review of the effects of exercise specifically in healthy populations has not yet been conducted. Given the demonstrated influence of exercise on neural plasticity, particularly cortical oscillatory activity, it is imperative to consolidate research on this phenomenon. Therefore, this review aims to summarize numerous PE studies on neuromodulatory mechanisms in the brain over the past decade, covering (1) effects of resistance and aerobic training on brain health via neural oscillations; (2) how mind-body exercise affects human neural activity and cognitive functioning; (3) age-Related effects of PE on brain health and neurodegenerative disease rehabilitation via neural oscillation mechanisms; and (4) conclusion and future direction. In conclusion, the effect of PE on cortical activity is a multifaceted process, and this review seeks to comprehensively examine and summarize existing studies\' understanding of how PE regulates neural activity in the brain, providing a more scientific theoretical foundation for the development of personalized PE programs and further research.

OBJECTIVE: Schizophrenia (SCZ) is a globally prevalent, severe chronic mental disorder, with cognitive dysfunction being one of its core symptoms. Notably, overweight is exceedingly common among individuals with SCZ, and overweight can also impact cognitive function. Therefore, the relationship between overweight and cognition in SCZ is a clinical issue that is in need of research attention.
METHODS: This study enrolled 77 patients with SCZ, including 36 overweight and 41 non-overweight patients. The Positive and Negative Syndrome Scale (PANSS) was used to assess symptom severity, while cognitive functions were evaluated using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Electroencephalography (EEG) testing was performed, with power spectral analysis conducted across various frequency bands (δ, θ, α, β, low γ, and high γ).
RESULTS: Compared to non-overweight SCZ patients, those overweight exhibited significantly lower RBANS total and index scores in immediate memory, visuospatial/constructional abilities, and delayed memory. EEG spectral analysis revealed that overweight SCZ patients demonstrated significantly lower oscillation power ratios in the β, low γ, and high γ frequency bands compared to their non-overweight counterparts. Correlation analyses indicated a significant positive relationship between β wave activity and RBANS total scores among overweight SCZ patients, suggesting that reduced β power correlates with more severe cognitive dysfunction.
CONCLUSIONS: Our findings indicate that overweight SCZ patients experience more severe cognitive impairments in a resting state than those who are not overweight, with significant differences in EEG spectrum observed in the β and γ frequency bands. Additionally, our study establishes a correlation between various EEG spectrum dimensions and cognition. This research highlights the effects of overweight on cognition in individuals with SCZ. Additionally, employing EEG technology to study cognitive function in overweight SCZ patients can offer valuable electrophysiological insights.

Electroencephalographic Neurofeedback Training (EEG NFT) aims to improve sport performance by teaching athletes to control their mental states, leading to better cognitive, emotional, and physical outcomes. The psychomotor efficiency hypothesis suggests that optimizing brain function could enhance athletic ability, indicating the potential of EEG NFT. However, evidence for EEG-NFT\'s ability to alter critical brain activity patterns, such as sensorimotor rhythm and frontal midline theta-key for concentration and relaxation-is not fully established. Current research lacks standardized methods and comprehensive studies. This shortfall is due to inconsistent EEG target selection and insufficient focus on coherence in training. This review aims to provide empirical support for EEG target selection, conduct detailed control analyses, and examine the specificity of electrodes and frequencies to relation to the psychomotor efficiency hypothesis. Following the PRISMA method, 2,869 empirical studies were identified from PubMed, Science Direct, Web of Science, Embase, CNKI, and PsycINFO. Thirteen studies met the inclusion criteria: (i) proficient skill levels; (ii) use of EEG; (iii) neurofeedback training (NFT); (iv) motor performance metrics (reaction time, precision, dexterity, balance); (v) control group for NFT comparison; (vi) peer-reviewed English-language publication; and (vii) randomized controlled trial (RCT) design. Studies indicate that NFT can enhance sports performance, including improvements in shooting accuracy, golf putting, and overall motor skills, as supported by the psychomotor efficiency hypothesis. EEG NFT demonstrates potential in enhancing sports performance by optimizing performers\' mental states and psychomotor efficiency. However, the current body of research is hampered by inconsistent methodologies and a lack of standardized EEG target selection. To strengthen the empirical evidence supporting EEG NFT, future studies need to focus on standardizing target selection, employing rigorous control analyses, and investigating underexplored EEG markers. These steps are vital to bolster the evidence for EEG NFT and enhance its effectiveness in boosting sport performance.

Generalized epilepsy (GE) encompasses a heterogeneous group of hyperexcitability disorders that clinically manifest as seizures. At the whole-brain level, distinct seizure patterns as well as interictal epileptic discharges (IEDs) reflect key signatures of hyperexcitability in magneto- and electroencephalographic (M/EEG) recordings. Moreover, it had been suggested that aperiodic activity, specifically the slope of the 1/ƒx decay function of the power spectrum, might index neural excitability. However, it remained unclear if hyperexcitability as encountered at the cellular level directly translates to putative large-scale excitability signatures, amenable to M/EEG. In order to test whether the power spectrum is altered in hyperexcitable states, we recorded resting-state MEG from male and female GE patients (n = 51; 29 females; 28.82 ± 12.18 years; mean ± SD) and age-matched healthy controls (n = 49; 22 females; 32.10 ± 12.09 years). We parametrized the power spectra using FOOOF (\"fitting oscillations and one over f\") to separate oscillatory from aperiodic activity to directly test whether aperiodic activity is systematically altered in GE patients. We further identified IEDs to quantify the temporal dynamics of aperiodic activity around overt epileptic activity. The results demonstrate that aperiodic activity indexes hyperexcitability in GE at the whole-brain level, especially during epochs when no IEDs were present (p = 0.0130; d = 0.52). Upon IEDs, large-scale circuits transiently shifted to a less excitable network state (p = 0.001; d = 0.68). In sum, these results uncover that MEG background activity might index hyperexcitability based on the current brain state and does not rely on the presence of epileptic waveforms.

Human mobility requires neurocognitive inputs to safely navigate the environment. Previous research has examined neural processes that underly walking using mobile neuroimaging technologies, yet few studies have incorporated true real-world methods without a specific task imposed on participants (e.g., dual-task, motor demands). The present study included 40 young adults (M = 22.60, SD = 2.63, 24 female) and utilized mobile electroencephalography (EEG) to examine and compare theta, alpha, and beta frequency band power (μV2) during sitting and walking in laboratory and real-world environments. EEG data was recorded using the Muse S brain sensing headband, a portable system equipped with four electrodes (two frontal, two temporal) and one reference sensor. Qualitative data detailing the thoughts of each participant were collected after each condition. For the quantitative data, a 2 × 2 repeated measures ANOVA with within subject factors of environment and mobility was conducted with full participant datasets (n = 17, M = 22.59, SD = 2.97, 10 female). Thematic analysis was performed on the qualitative data (n = 40). Our findings support that mobility and environment may modulate neural activity, as we observed increased brain activation for walking compared to sitting, and for real-world walking compared to laboratory walking. We identified five qualitative themes across the four conditions 1) physical sensations and bodily awareness, 2) responsibilities and planning, 3) environmental awareness, 4) mobility, and 5) spotlight effect. Our study highlights the importance and potential for real-world methods to supplement standard research practices to increase the ecological validity of studies conducted in the fields of neuroscience and kinesiology.

Recent studies using resting-state functional magnetic resonance imaging have shown that loneliness is associated with altered blood oxygenation in several brain regions. However, the relationship between loneliness and changes in neuronal rhythm activity in the brain remains unclear. To evaluate brain rhythm, we conducted an exploratory resting-state electroencephalogram (EEG) study of loneliness. We recorded resting-state EEG signals from 139 participants (94 women; mean age = 19.96 years) and analyzed power spectrum density (PSD) and functional connectivity (FC) in both the electrode and source spaces. The PSD analysis revealed significant correlations between loneliness scores and decreased beta-band powers, which may indicate negative emotion, attention, reward, and/or sensorimotor processing. The FC analysis revealed a trend of alpha-band FC associated with individuals\' loneliness scores. These findings provide new insights into the neural basis of loneliness, which will facilitate the development of neurobiologically informed interventions for loneliness.

BACKGROUND: \"Metacontrol\" describes the ability to maintain an optimal balance between cognitive control styles that are either more persistent or more flexible. Recent studies have shown a link between metacontrol and aperiodic EEG patterns. The present study aimed to gain more insight into the neurobiological underpinnings of metacontrol by using methylphenidate (MPH), a compound known to increase postsynaptic catecholamine levels and modulate cortical noise.
METHODS: In a double-blind, randomized, placebo-controlled study design, we investigated the effect of MPH (0.5 mg/kg) on aperiodic EEG activity during a flanker task in a sample of n = 25 neurotypical adults. To quantify cortical noise, we employed the fitting oscillations and one over f algorithm.
RESULTS: Compared with placebo, MPH increased the aperiodic exponent, suggesting that it reduces cortical noise in 2 ways. First, it did so in a state-like fashion, as the main effect of the drug was visible and significant in both pre-trial and within-trial periods. Second, the electrode-specific analyses showed that the drug also affects specific processes by dampening the downregulation of noise in conditions requiring more control.
CONCLUSIONS: Our findings suggest that the aperiodic exponent provides a neural marker of metacontrol states and changes therein. Further, we propose that the effectiveness of medications targeting catecholaminergic signaling can be evaluated by studying changes of cortical noise, fostering the idea of using the quantification of cortical noise as an indicator in pharmacological treatment.