Objective.Although motor imagery-based brain-computer interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity\'s significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state electroencephalography (EEG) network differs between BCI-literacy and -illiteracy.Approach.To address the issues above, we analyzed three large public EEG datasets using three functional connectivity and three effective connectivity metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for multivariate Granger causality. The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found.Significance.Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction\'s accuracy.

Recent works point to the importance of emotions in special-numerical associations. There remains a notable gap in understanding the electrophysiological underpinnings of such associations. Exploring resting-state (rs) EEG, particularly in frontal regions, could elucidate emotional aspects, while other EEG measures might offer insights into the cognitive dimensions correlating with behavioral performance. The present work investigated the relationship between rs-EEG measures (emotional and cognitive traits) and performance in the mental number line (MNL). EEG activity in theta (3-7 Hz), alpha (8-12 Hz, further subdivided into low-alpha and high-alpha), sensorimotor rhythm (SMR, 13-15 Hz), beta (16-25 Hz), and high-beta/gamma (28-40 Hz) bands was assessed. 76 university students participated in the study, undergoing EEG recordings at rest before engaging in a computerized number-to-position (CNP) task. Analysis revealed significant associations between frontal asymmetry, specific EEG frequencies, and MNL performance metrics (i.e., mean direction bias, mean absolute error, and mean reaction time). Notably, theta and beta asymmetries correlated with direction bias, while alpha peak frequency (APF) and beta activity related to absolute errors in numerical estimation. Moreover, the study identified significant correlations between relative amplitude indices (i.e., theta/beta ratio, theta/SMR ratio) and both absolute errors and reaction times (RTs). Our findings offer novel insights into the emotional and cognitive aspects of EEG patterns and their links to MNL performance.

Although exercise training has been shown to enhance neurological function, there is a shortage of research on how exercise training affects the temporal-spatial synchronization properties of functional networks, which are crucial to the neurological system. This study recruited 23 professional and 24 amateur dragon boat racers to perform simulated paddling on ergometers while recording EEG. The spatiotemporal dynamics of the brain were analyzed using microstates and omega complexity. Temporal dynamics results showed that microstate D, which is associated with attentional networks, appeared significantly altered, with significantly higher duration, occurrence, and coverage in the professional group than in the amateur group. The transition probabilities of microstate D exhibited a similar pattern. The spatial dynamics results showed the professional group had lower brain complexity than the amateur group, with a significant decrease in omega complexity in the α (8-12 Hz) and β (13-30 Hz) bands. Dragon boat training may strengthen the attentive network and reduce the complexity of the brain. This study provides evidence that dragon boat exercise improves the efficiency of the cerebral functional networks on a spatiotemporal scale.

The present study explored whether, given the association of temporal alpha with fear circuitry (learning and conditioning), exposure to complex childhood trauma (CCT) is reflected in the temporal-posterior alpha power in resting-state electroencephalography (EEG) in complex trauma-exposed adolescents in a sample of 25 adolescents and similar controls aged 12-17 years. Both trauma and psychopathology were screened or assessed, and resting-state EEG was recorded following a preregistered protocol for data collection. Temporal-posterior alpha power, corresponding to the T5 and T6 electrode locations (international 10-20 system), was extracted from resting-state EEG in both eyes-open and eyes-closed conditions. We found that in the eyes-open condition, temporal-posterior alpha was significantly lower in adolescents exposed to CCT relative to healthy controls, suggesting that childhood trauma exposure may have a measurable impact on alpha oscillatory patterns. Our study highlights the importance of considering potential neural markers, such as temporal-posterior alpha power, to understanding the long-term consequences of CCT exposure in developmental samples, with possible important clinical implications in guiding neuroregulation interventions.

BACKGROUND: Studies that use nonlinear methods to identify abnormal brain dynamics in patients with psychiatric disorders are limited. This study investigated brain dynamics based on EEG using multiscale entropy (MSE) analysis in patients with schizophrenia (SZ) and bipolar disorder (BD).
METHODS: The eyes-closed resting-state EEG data were collected from 51 patients with SZ, 51 patients with BD, and 51 healthy controls (HCs). Patients with BD were further categorized into type I (n = 23) and type II (n = 16), and then compared with patients with SZ. A sample entropy-based MSE was evaluated from the bilateral frontal, central, and parieto-occipital regions using 30-s artifact-free EEG data for each individual. Correlation analyses of MSE values and psychiatric symptoms were performed.
RESULTS: For patients with SZ, higher MSE values were observed at higher-scale factors (i.e., 41-70) across all regions compared with both HCs and patients with BD. Furthermore, there were positive correlations between the MSE values in the left frontal and parieto-occipital regions and PANSS scores. For patients with BD, higher MSE values were observed at middle-scale factors (i.e., 13-40) in the bilateral frontal and central regions compared with HCs. Patients with BD type I exhibited higher MSE values at higher-scale factors across all regions compared with those with BD type II. In BD type I, positive correlations were found between MSE values in all left regions and YMRS scores.
CONCLUSIONS: Patients with psychiatric disorders exhibited group-dependent MSE characteristics. These results suggest that MSE features may be useful biomarkers that reflect pathophysiological characteristics.

UNASSIGNED: Fragile X Syndrome (FXS) is the leading monogenic cause of intellectual disability and autism spectrum disorder. Currently, there are no established biomarkers for predicting and monitoring drug effects in FXS, and no approved therapies are available. Previous studies have shown electrophysiological changes in the brain using electroencephalography (EEG) in individuals with FXS and animal models. These changes may be influenced by drug therapies. In this study, we aimed to assess the reliability of resting-state EEG measures in individuals with FXS, which could potentially serve as a biomarker for drug discovery.
UNASSIGNED: We collected resting-state EEG data from 35 individuals with FXS participating in placebo-controlled clinical trials (23 males, 12 females; visit age mean+/-std 25.6 +/-8.3). The data were analyzed for various spectral features using intraclass correlation analysis to evaluate test-retest reliability. The intervals between EEG recordings ranged from same-day measurements to up to six weeks apart.
UNASSIGNED: Our results showed high reliability for most spectral features, with same-day reliability exceeding 0.8. Features of interest demonstrated ICC values of 0.60 or above at longer intervals. Among the features, alpha band relative power exhibited the highest reliability.
UNASSIGNED: These findings indicate that resting-state EEG can provide consistent and reproducible measures of brain activity in individuals with FXS. This supports the potential use of EEG as an objective biomarker for evaluating the effects of new drugs in FXS.
UNASSIGNED: The reliable measurements obtained from power spectrum-based resting-state EEG make it a promising tool for assessing the impact of small molecule drugs in FXS.

Over time, pathological, genetic, environmental, and lifestyle factors can age the brain and diminish its functional capabilities. While these factors can lead to disorders that can be diagnosed and treated once they become symptomatic, often treatment is difficult or ineffective by the time significant overt symptoms appear. One approach to this problem is to develop a method for assessing general age-related brain health and function that can be implemented widely and inexpensively. To this end, we trained a machine-learning algorithm on resting-state EEG (RS-EEG) recordings obtained from healthy individuals as the core of a brain-age estimation technique that takes an individual\'s RS-EEG recorded with the low-cost, user-friendly EMOTIV EPOC X headset and returns that person\'s estimated brain age. We tested the current version of our machine-learning model against an independent test-set of healthy participants and obtained a correlation coefficient of 0.582 between the chronological and estimated brain ages (r = 0.963 after statistical bias-correction). The test-retest correlation was 0.750 (0.939 after bias-correction) over a period of 1 week. Given these strong results and the ease and low cost of implementation, this technique has the potential for widespread adoption in the clinic, workplace, and home as a method for assessing general brain health and function and for testing the impact of interventions over time.

Attentional control theory suggests that test anxiety hinders individuals\' attentional control, aiding our understanding of how test anxiety may impair cognitive function. However, various methods used to assess attentional control have yielded inconsistent findings. Moreover, past studies, especially on adolescents, that examine the distinct impacts of worry and the emotional components of test anxiety on individuals\' attentional control capacity are scarce. This study, using self-report, behavioral, and resting-state EEG measures, explores how worry and emotionality, impact attentional control in adolescents. It enhances our understanding of the link between test anxiety and cognitive function. Referring to the effect size from prior studies, a total of 42 adolescents took part in the study. We used the Test Anxiety Inventory, due to it can assess worry and emotionality components. We employed three widely-utilized measures of attentional control: the Attentional Control Scale (ACS), the Go/Nogo task, and resting-state electroencephalography measures (alpha oscillation and the theta/beta power ratio). Both worry and emotionality components were significantly and negatively correlated with the ACS scores. Unlike worry, emotionality demonstrated a significant positive correlation with response times for the Go trials and alpha power in the parietal cortex. These results suggest that, emotionality, but not worry, is highly correlated with attentional control deficits in adolescents. This study underscores the significance of distinguishing between the components of test anxiety, which aids in comprehending the negative impacts of test anxiety on adolescents\' academic performance.

Microstate analysis is a spatiotemporal method where instantaneous scalp potential topography represents the current state of the brain. The temporal evolution of these scalp topographies gives an understanding of quasi-stable periods of long-range coherence between distant electrodes, reflecting functional coordination within large-scale cortical networks. It has been proven potential in identification and characterization of neurophysiological indicators associated with neuropsychiatric conditions. Changes in microstates connected to symptoms and cognitive impairments of neuropsychiatric conditions. It is useful in the study of cognitive processes and disorders related to memory. Researchers may probe into the relationships between microstates and other cognitive processes, such as memory retrieval and encoding. This is a tool for clinicians to enhance the precision of diagnosis and inform possibilities for treatment by acquiring information regarding individual diversity in microstates could lead to tailored medical methods. Customizing treatment according to a patient\'s microstate patterns could improve the efficacy of treatment. The papers selected for the review span a broad-spectrum including memory related disorders, psychiatry and neurological disorders. A section in the review article has been dedicated to source localization of EEG microstates. The selection of review papers shed light on the importance and huge potential of application of EEG microstate analysis in various neuropsychological processes. The review concludes with the need for standardization of microstate analysis. It suggests the incorporation of widely accepted machine learning techniques for increasing the accuracy, reliability and acceptability of microstate analysis as reliable biomarkers for neurological conditions in the future.

OBJECTIVE: Idiopathic/isolated REM-sleep behavior disorder (iRBD) often precedes the onset of synucleinopathies. Here, we investigated whether baseline resting-state EEG advanced spectral power and functional connectivity differ between iRBD patients who converted towards a synucleinopathy at follow-up and those who did not.
METHODS: Eighty-one participants with iRBD (66.89±6.91 years) underwent a baseline resting-state EEG recording, a neuropsychological assessment and a neurological examination. We estimated EEG power spectral density using standard analyses and derived spectral estimates of rhythmic and arrhythmic components. Global and pairwise EEG functional connectivity analyses were computed using the weighted phase-lag index (wPLI). Pixel-based permutation tests were used to compare groups.
RESULTS: After a mean follow-up of 5.01±2.76 years, 34 patients were diagnosed with a synucleinopathy (67.81±7.34 years) and 47 remained disease-free (65.53±7.09 years). Among patients who converted, 22 were diagnosed with Parkinson\'s disease and 12 with dementia with Lewy bodies. As compared to patients who did not convert, patients who converted exhibited at baseline higher relative theta standard power, steeper slopes of the arrhythmic component and higher theta rhythmic power mostly in occipital regions. Furthermore, patients who converted showed higher beta global wPLI but lower alpha wPLI between left temporal and occipital regions.
CONCLUSIONS: Analyses of resting-state EEG rhythmic and arrhythmic components and functional connectivity suggest an imbalanced excitatory-to-inhibitory activity within large-scale networks, which is associated with later development of a synucleinopathy in iRBD patients.