Sustained attention operates via dissociable neural mechanisms across different eccentric locations.

In primates, foveal and peripheral vision have distinct neural architectures and functions. However, it has been debated if selective attention operates via the same or different neural mechanisms across eccentricities. We tested these alternative accounts by examining the effects of selective attention on the steady-state visually evoked potential (SSVEP) and the fronto-parietal signal measured via EEG from human subjects performing a sustained visuospatial attention task. With a negligible level of eye movements, both SSVEP and SND exhibited the heterogeneous patterns of attentional modulations across eccentricities. Specifically, the attentional modulations of these signals peaked at the parafoveal locations and such modulations wore off as visual stimuli appeared closer to the fovea or further away towards the periphery. However, with a relatively higher level of eye movements, the heterogeneous patterns of attentional modulations of these neural signals were less robust. These data demonstrate that the top-down influence of covert visuospatial attention on early sensory processing in human cortex depends on eccentricity and the level of saccadic responses. Taken together, the results suggest that sustained visuospatial attention operates differently across different eccentric locations, providing new understanding of how attention augments sensory representations regardless of where the attended stimulus appears.

ZleepAnlystNet: a novel deep learning model for automatic sleep stage scoring based on single-channel raw EEG data using separating training.

Numerous models for sleep stage scoring utilizing single-channel raw EEG signal have typically employed CNN and BiLSTM architectures. While these models, incorporating temporal information for sequence classification, demonstrate superior overall performance, they often exhibit low per-class performance for N1-stage, necessitating an adjustment of loss function. However, the efficacy of such adjustment is constrained by the training process. In this study, a pioneering training approach called separating training is introduced, alongside a novel model, to enhance performance. The developed model comprises 15 CNN models with varying loss function weights for feature extraction and 1 BiLSTM for sequence classification. Due to its architecture, this model cannot be trained using an end-to-end approach, necessitating separate training for each component using the Sleep-EDF dataset. Achieving an overall accuracy of 87.02%, MF1 of 82.09%, Kappa of 0.8221, and per-class F1-socres (W 90.34%, N1 54.23%, N2 89.53%, N3 88.96%, and REM 87.40%), our model demonstrates promising performance. Comparison with sleep technicians reveals a Kappa of 0.7015, indicating alignment with reference sleep stags. Additionally, cross-dataset validation and adaptation through training with the SHHS dataset yield an overall accuracy of 84.40%, MF1 of 74.96% and Kappa of 0.7785 when tested with the Sleep-EDF-13 dataset. These findings underscore the generalization potential in model architecture design facilitated by our novel training approach.

High-altitude balloon platform for studying the biological response of living organisms exposed to near-space environments.

The intangible desire to explore the mysteries of the universe has driven numerous advancements for humanity for centuries. Extraterrestrial journeys are becoming more realistic as a result of human curiosity and endeavors. Over the years, space biology research has played a significant role in understanding the hazardous effects of the space environment on human health during long-term space travel. The inevitable consequence of a space voyage is space ionizing radiation, which has deadly aftereffects on the human body. The paramount objective of this study is to provide a robust platform for performing biological experiments within the Earth's stratosphere by utilizing high-altitude balloons. This platform allows the use of a biological payload to simulate spaceflight missions within the unique properties of space that cannot be replicated in terrestrial facilities. This paper describes the feasibility and demonstration of a biological balloon mission suitable for students and scientists to perform space biology experiments within the boundary of the stratosphere. In this study, a high-altitude balloon was launched into the upper atmosphere (∼29 km altitude), where living microorganisms were exposed to a hazardous combination of UV irradiation, ultralow pressure and cold shock. The balloon carried the budding yeast Saccharomyces cerevisiae to investigate microbial survival potential under extreme conditions. The results indicated a notable reduction in biosample mortality two orders of magnitude (2-log) after exposure to 164.9 kJ m-2 UV. Postflight experiments have shown strong evidence that the effect of UV irradiation on living organisms is stronger than that of other extreme conditions.

Digital Group-Based Intervention for Physical Activity Promotion Among Thai Adults During the COVID-19 Lockdown: Randomized Controlled Trial.

BACKGROUND: The COVID-19 pandemic significantly diminished the physical activity (PA) level of Thai adults belonging to Generation Y (Gen Y). As a response to the global crisis, many individuals worldwide have turned to social community platforms, recognizing their potential in promoting PA during the pandemic. Gen Y, in particular, demonstrates exceptional proficiency in using social media platforms, showcasing a remarkable aptitude for swiftly accessing new information and knowledge. However, their proclivity for reckless behavior exposes them to various health risks, potentially leading to enduring adverse health consequences. Consequently, there arises a pressing need to develop a comprehensive model aimed at elevating the PA levels among individuals belonging to Gen Y. OBJECTIVE: This research aimed to examine the effectiveness of a digital group-based activity in promoting PA among Gen Y in Thailand. METHODS: This was a parallel 2-arm randomized controlled trial with single-blind allocation to experimental and control groups and pre- and posttest measurements. Measurements were administered on the web and were designed for respondents to complete by themselves. The sample comprised 100 Gen Y individuals who met the inclusion criteria. Both groups were matched for background characteristics. The two 8-week intervention activities were (1) two weeks of education and (2) six weeks of motivation by target groups that set goals for PA together (using the Zoom meeting application), with a time limit and group consensus as to when the goal was to be achieved. The intervention activities were implemented one by one at specified intervals and delivered daily through health apps and the official LINE account. RESULTS: The intervention starts from August 22 to October 16, 2021. Of the 100 participants, 20 (20%) left the study, and the remaining 80 (80%) participated in the study (40 individuals each in the experimental and control groups). After participating in the experiment, a statistically significant difference in PA was found between the 2 groups (moderate to vigorous PA; 25/40, 63%; P=.03). Participants in the intervention group collected a higher cumulative minute of moderate to vigorous PA weekly (283 minutes) than those in the control group (164 minutes), and this was statistically significant (P=.03). For the transition to the fourth stage of behavior (ie, action), the improvement in the experimental group, after participating in the trial, was statistically significant compared to that of the control group (P=.01). CONCLUSIONS: Digital group-based activity showed its effectiveness in improving the PA of Gen Y individuals in the intervention group. It created a process-based intervention activity that corresponds to the stages of behavior changes, from contemplation to action. The digital community can also connect individuals to comparable groups locally and globally. TRIAL REGISTRATION: Thai Clinical Trials Registry TCTR20211101005; https://www.thaiclinicaltrials.org/show/TCTR20211101005.

Sustained attention operates via dissociable neural mechanisms across different eccentric locations.

In primates, foveal and peripheral vision have distinct neural architectures and functions. However, it has been debated if selective attention operates via the same or different neural mechanisms across eccentricities. We tested these alternative accounts by examining the effects of selective attention on the steady-state visually evoked potential (SSVEP) and the fronto-parietal signal measured via EEG from human subjects performing a sustained visuospatial attention task. With a negligible level of eye movements, both SSVEP and SND exhibited the heterogeneous patterns of attentional modulations across eccentricities. Specifically, the attentional modulations of these signals peaked at the parafoveal locations and such modulations wore off as visual stimuli appeared closer to the fovea or further away towards the periphery. However, with a relatively higher level of eye movements, the heterogeneous patterns of attentional modulations of these neural signals were less robust. These data demonstrate that the top-down influence of covert visuospatial attention on early sensory processing in human cortex depends on eccentricity and the level of saccadic responses. Taken together, the results suggest that sustained visuospatial attention operates differently across different eccentric locations, providing new understanding of how attention augments sensory representations regardless of where the attended stimulus appears.

QEEG characteristics associated with malnutrition-inflammation complex syndrome.

End-stage renal disease (ESRD) has been linked to cerebral complications due to the comorbidity of malnutrition and inflammation, which is referred to as malnutrition-inflammation complex syndrome (MICS). The severity of this condition is clinically assessed with the malnutrition-inflammation score (MIS), and a cutoff of five is used to optimally distinguish patients with and without MICS. However, this tool is still invasive and inconvenient, because it combines medical records, physical examination, and laboratory results. These steps require clinicians and limit MIS usage on a regular basis. Cerebral diseases in ESRD patients can be evaluated reliably and conveniently by using quantitative electroencephalogram (QEEG), which possibly reflects the severity of MICS likewise. Given the links between kidney and brain abnormalities, we hypothesized that some QEEG patterns might be associated with the severity of MICS and could be used to distinguish ESRD patients with and without MICS. Hence, we recruited 62 ESRD participants and divided them into two subgroups: ESRD with MICS (17 women (59%), age 60.31 ± 7.79 years, MIS < 5) and ESRD without MICS (20 women (61%), age 62.03 ± 9.29 years, MIS ≥ 5). These participants willingly participated in MIS and QEEG assessments. We found that MICS-related factors may alter QEEG characteristics, including the absolute power of the delta, theta, and beta 1 bands, the relative power of the theta and beta 3 subbands, the coherence of the delta and theta bands, and the amplitude asymmetry of the beta 1 band, in certain brain regions. Although most of these QEEG patterns are significantly correlated with MIS, the delta absolute power, beta 1 amplitude asymmetry, and theta coherence are the optimal inputs for the logistic regression model, which can accurately classify ESRD patients with and without MICS (90.0 ± 5.7% area under the receiver operating characteristic curve). We suggest that these QEEG features can be used not only to evaluate the severity of cerebral disorders in ESRD patients but also to noninvasively monitor MICS in clinical practice.

Comparing performance measures and clinical outcomes between mobile stroke units and usual care in underserved areas.

BACKGROUND: The efficacy of mobile stroke units (MSUs) in improving acute ischemic stroke (AIS) care in developing countries is unknown. We compared performance measures and stroke outcomes in AIS patients between MSU and usual care: emergency medical services (EMS) and walk-in. METHODS: We enrolled patients > 18 years of age with an AIS within 4.5 h after onset. Demographic data, types, and time of reperfusion therapies and clinical outcomes were recorded. A favorable outcome was defined as a modified Rankin Scale (mRS) 0-2 at 3 months. RESULTS: A total of 978 AIS patients (MSU = 243, EMS = 214, walk-in = 521) were enrolled between June 1, 2018, and April 30, 2021. The mean age (± SD) was 66 (± 14) years, and 510 (52.1%) were male. AIS time metrics were the shortest in the MSU with a mean (± SD) door to needle (DN) time of 20 (± 7), 29 (± 13), and 35 (± 16) min (p < 0.001) and door to puncture (DP) time of 73 ± 19, 86 ± 33, and 101 ± 42 min (p < 0.001) in MSU, EMS, and walk-in, respectively. Participants in the MSU (56.8%) received higher rate of reperfusion therapie(s) when compared to the EMS (51.4%) and walk-in (31.5%) (p < 0.001). After adjustment for any potential confounders and using the EMS as a reference, the MSU has the highest likelihood of achieving a favorable outcome (adjusted OR 2.15; 95% CI 1.39-3.32). CONCLUSIONS: In underserved populations, MSUs significantly reduced DN time, increased the likelihood of receiving reperfusion treatment, and achieved independency at 3 months when compared to usual care.

Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network.

Humans experience a variety of emotions throughout the course of their daily lives, including happiness, sadness, and rage. As a result, an effective emotion identification system is essential for electroencephalography (EEG) data to accurately reflect emotion in real-time. Although recent studies on this problem can provide acceptable performance measures, it is still not adequate for the implementation of a complete emotion recognition system. In this research work, we propose a new approach for an emotion recognition system, using multichannel EEG calculation with our developed entropy known as multivariate multiscale modified-distribution entropy (MM-mDistEn) which is combined with a model based on an artificial neural network (ANN) to attain a better outcome over existing methods. The proposed system has been tested with two different datasets and achieved better accuracy than existing methods. For the GAMEEMO dataset, we achieved an average accuracy ± standard deviation of 95.73% ± 0.67 for valence and 96.78% ± 0.25 for arousal. Moreover, the average accuracy percentage for the DEAP dataset reached 92.57% ± 1.51 in valence and 80.23% ± 1.83 in arousal.

MINERVA: A CubeSat for demonstrating DNA damage mitigation against space radiation in C. elegans by using genetic modification.

The ideas of deep-space human exploration, interplanetary travel, and space civilizations are becoming a reality. However, numerous hindrances remain standing in the way of accomplishing these feats, one of which is space ionizing radiation. Space ionizing radiation has become the most hazardous health risk for long-term human space exploration, as it can induce chromosomal damage and epigenetic changes. The Minerva mission aims to demonstrate cutting-edge technology to inhibit DNA damage against deep-space radiation exposure by using genetic modification. The concept of the experiment is to transform a creature with radiation intolerance into a transgenic organism that is radiation-tolerant. In this mission, Caenorhabditis elegans (C. elegans) will be genetically engineered with a protein-coding gene associated with DNA damage protection called damage suppressor (Dsup). Dsup is a nucleosome-binding protein from the tardigrade Ramazzottius varieornatus that has a unique ability to prevent DNA damage. This paper describes the feasibility of Minerva CubeSat, which will venture out to cis-lunar orbit with a biosensor payload capable of sustaining and culturing C. elegans under space environment conditions for 4 months. The mission will set in motion a paradigm shift corresponding to future space medicines and how they will be developed in the future, introducing a platform suitable for future experiments in the fields of space biology. Ultimately, the paramount objective of Minerva will be to test the limits of genetic engineering and incorporate it into the arduous journey of human perseverance to overcome the boundaries of space exploration-a vital step in making Mars colonization safe.

Impact of Interfractional Error on Dosiomic Features.

Objectives: The purpose of this study was to investigate the stability of dosiomic features under random interfractional error. We investigated the differences in the values of features with different fractions and the error in the values of dosiomic features under interfractional error. Material and Methods: The isocenters of the treatment plans of 15 lung cancer patients were translated by a maximum of ±3 mm in each axis with a mean of (0, 0, 0) and a standard deviation of (1.2, 1.2, 1.2) mm in the x, y, and z directions for each fraction. A total of 81 dose distributions for each patient were then calculated considering four fraction number groups (2, 10, 20, and 30). A total of 93 dosiomic features were extracted from each dose distribution in four different regions of interest (ROIs): gross tumor volume (GTV), planning target volume (PTV), heart, and both lungs. The stability of dosiomic features was analyzed for each fraction number group by the coefficient of variation (CV) and intraclass correlation coefficient (ICC). The agreements in the means of dosiomic features among the four fraction number groups were tested by ICC. The percent differences (PD) between the dosiomic features extracted from the original dose distribution and the dosiomic features extracted from the dose distribution with interfractional error were calculated. Results: Eleven out of 93 dosiomic features demonstrated a large CV (CV ≥ 20%). Overall CV values were highest in GTV ROIs and lowest in lung ROIs. The stability of dosiomic features decreased as the total number of fractions decreased. The ICC results showed that five out of 93 dosiomic features had an ICC lower than 0.75, which indicates intermediate or poor stability under interfractional error. The mean dosiomic feature values were shown to be consistent with different numbers of fractions (ICC ≥ 0.9). Some of the dosiomic features had PD greater than 50% and showed different PD values with different numbers of fractions. Conclusion: Some dosiomic features have low stability under interfractional error. The stability and values of the dosiomic features were affected by the total number of fractions. The effect of interfractional error on dosiomic features should be considered in further studies regarding dosiomics for reproducible results.

Radiomic and Dosiomic Features for the Prediction of Radiation Pneumonitis Across Esophageal Cancer and Lung Cancer.

PURPOSE: The aim was to investigate the advantages of dosiomic and radiomic features over traditional dose-volume histogram (DVH) features for predicting the development of radiation pneumonitis (RP), to validate the generalizability of dosiomic and radiomic features by using features selected from an esophageal cancer dataset and to use these features with a lung cancer dataset. MATERIALS AND METHODS: A dataset containing 101 patients with esophageal cancer and 93 patients with lung cancer was included in this study. DVH and dosiomic features were extracted from 3D dose distributions. Radiomic features were extracted from pretreatment CT images. Feature selection was performed using only the esophageal cancer dataset. Four predictive models for RP (DVH, dosiomic, radiomic and dosiomic + radiomic models) were compared on the esophageal cancer dataset. We further used a lung cancer dataset for the external validation of the selected dosiomic and radiomic features from the esophageal cancer dataset. The performance of the predictive models was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve (ROCAUC) and the AUC of the precision recall curve (PRAUC) metrics. RESULT: The ROCAUCs and PRAUCs of the DVH, dosiomic, radiomic and dosiomic + radiomic models on esophageal cancer dataset were 0.67 ± 0.11 and 0.75 ± 0.10, 0.71 ± 0.10 and 0.77 ± 0.09, 0.71 ± 0.11 and 0.79 ± 0.09, and 0.75 ± 0.10 and 0.81 ± 0.09, respectively. The predictive performance of the dosiomic- and radiomic-based models was significantly higher than that of the DVH-based model with respect to esophageal cancer. The ROCAUCs and PRAUCs of the DVH, dosiomic, radiomic and dosiomic + radiomic models on the lung cancer dataset were 0.64 ± 0.18 and 0.37 ± 0.20, 0.67 ± 0.17 and 0.37 ± 0.20, 0.67 ± 0.16 and 0.45 ± 0.23, and 0.68 ± 0.16 and 0.44 ± 0.22, respectively. On the lung cancer dataset, the predictive performance of the radiomic and dosiomic + radiomic models was significantly higher than that of the DVH-based model. However, the PRAUC of the dosiomic-based model showed no significant difference relative to the corresponding RP prediction performance on the lung cancer dataset. CONCLUSION: The results suggested that dosiomic and CT radiomic features could improve RP prediction in thoracic radiotherapy. Dosiomic and radiomic feature knowledge might be transferrable from esophageal cancer to lung cancer.

Neuropsychological classification based on brain mapping performance in Thai children with and without ADHD.

This study investigated the cognitive strengths and weaknesses of Attention Deficit Hyperactivity Disorder (ADHD) patients by using quantitative electroencephalography (QEEG) to analyze brain mapping outcomes in relation to neuropsychological assessments. A total of 305 participants with and without ADHD were recruited. The theta relative power of QEEG was analyzed using stepwise multiple regression, and twelve scalp regions were compared between groups using independent t-tests. Increased power was found at the T5 scalp region in the ADHD group and at the F4 region in the non-ADHD group. Decreased power was found at the F7 region in the ADHD group and at the F3 region in the non-ADHD group. Six of the twelve brain locations had significantly higher theta relative power in the ADHD group than in the non-ADHD group. These brain mapping outcomes can be easily translated to neuropsychological functions, such as attention, executive function, memory, spatial ability, and language, to better understand or predict the potential behavior of ADHD patients.

Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy.

Epilepsy is a common neurological disease that affects a wide range of the world population and is not limited by age. Moreover, seizures can occur anytime and anywhere because of the sudden abnormal discharge of brain neurons, leading to malfunction. The seizures of approximately 30% of epilepsy patients cannot be treated with medicines or surgery; hence these patients would benefit from a seizure prediction system to live normal lives. Thus, a system that can predict a seizure before its onset could improve not only these patients' social lives but also their safety. Numerous seizure prediction methods have already been proposed, but the performance measures of these methods are still inadequate for a complete prediction system. Here, a seizure prediction system is proposed by exploring the advantages of multivariate entropy, which can reflect the complexity of multivariate time series over multiple scales (frequencies), called multivariate multiscale modified-distribution entropy (MM-mDistEn), with an artificial neural network (ANN). The phase-space reconstruction and estimation of the probability density between vectors provide hidden complex information. The multivariate time series property of MM-mDistEn provides more understandable information within the multichannel data and makes it possible to predict of epilepsy. Moreover, the proposed method was tested with two different analyses: simulation data analysis proves that the proposed method has strong consistency over the different parameter selections, and the results from experimental data analysis showed that the proposed entropy combined with an ANN obtains performance measures of 98.66% accuracy, 91.82% sensitivity, 99.11% specificity, and 0.84 area under the curve (AUC) value. In addition, the seizure alarm system was applied as a postprocessing step for prediction purposes, and a false alarm rate of 0.014 per hour and an average prediction time of 26.73 min before seizure onset were achieved by the proposed method. Thus, the proposed entropy as a feature extraction method combined with an ANN can predict the ictal state of epilepsy, and the results show great potential for all epilepsy patients.

Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients.

OBJECTIVE: The purpose of this study was to develop a model using dose volume histogram (DVH) and dosiomic features to predict the risk of radiation pneumonitis (RP) in the treatment of esophageal cancer with radiation therapy and to compare the performance of DVH and dosiomic features after adjustment for the effect of fractionation by correcting the dose to the equivalent dose in 2 Gy (EQD2). MATERIALS AND METHODS: DVH features and dosiomic features were extracted from the 3D dose distribution of 101 esophageal cancer patients. The features were extracted with and without correction to EQD2. A predictive model was trained to predict RP grade ≥ 1 by logistic regression with L1 norm regularization. The models were then evaluated by the areas under the receiver operating characteristic curves (AUCs). RESULT: The AUCs of both DVH-based models with and without correction of the dose to EQD2 were 0.66 and 0.66, respectively. Both dosiomic-based models with correction of the dose to EQD2 (AUC = 0.70) and without correction of the dose to EQD2 (AUC = 0.71) showed significant improvement in performance when compared to both DVH-based models. There were no significant differences in the performance of the model by correcting the dose to EQD2. CONCLUSION: Dosiomic features can improve the performance of the predictive model for RP compared with that obtained with the DVH-based model.

Background Activity Findings in End-Stage Renal Disease With and Without Comorbid Diabetes: An Electroencephalogram Study.

Renal failure and diabetes can induce cerebral complications, including encephalopathy, for which attentional and cognitive impairment are common symptoms. It is possible that renal failure with comorbid diabetes may induce more severe encephalopathy due to multiple pathogenic mechanisms. This concept was supported by the main findings of this study, which showed that EEG background activity between end-stage renal disease with and without comorbid diabetes was significantly different in relative power of delta in the eyes-open condition in frontoparietal regions; theta in the eyes-closed condition in all regions; beta in the parieto-occipital regions in both eye conditions; the delta/theta ratio in both eye conditions in frontoparietal regions; and the theta/beta ratio in all regions in the eyes-closed condition. These findings may increase awareness of comorbid cerebral complications in clinical practice. Moreover, the delta/theta ratio is recommended as an optimal feature to possibly determine the severity of encephalopathy.

Alterations of regional cerebral glucose metabolism using18F-fluorodeoxyglucose positron-emission tomography/computed tomography and electroencephalography analysis during mindfulness breathing in Anapanasati meditation: A preliminary analysis.

Anapanasati is a core meditation of a breath-centered practice in the Buddhist Theravada tradition, which may have some neurological mechanism effects on the brain. To gain insight into the neurological mechanisms involved in Anapanasati meditation, we measured the alterations of regional cerebral glucose metabolism during Anapanasati meditation using positron-emission tomography/computed tomography (PET/CT) and electroencephalography (EEG) analysis. This prospective study was conducted in six right-handed volunteer participants (two men, four women; aged: 32-67 years) who underwent18F-fluorodeoxyglucose (18F-FDG) PET/CT scans to compare the alterations of regional cerebral glucose metabolism during normal consciousness and Anapanasati meditation states. Spectral EEG analysis was performed throughout the investigations. Statistical parametric mapping was used for the18F-FDG PET/CT image analyses. The visual analysis demonstrated moderate-to-marked increased metabolism in posterior cingulate cortex in all six patients, while mild-to-moderate increased uptake in the whole frontal lobe was also observed in four patients and precuneus in four patients. Meanwhile, the semiquantitative analysis yielded an increase of regional cerebral glucose metabolism in the right mid-to-posterior cingulate gyrus (P < 0.000), with visible alpha waves on the frontal of the EEG findings. Our semiquantitative analysis showed a significantly increased metabolism only in the posterior cingulate cortex, but visually, there was also an increased metabolism in the whole frontal lobe in most of the patients correlating with EEG findings.

Quantitative EEG in sports: performance level estimation of professional female soccer players.

PURPOSE: Measuring the peak performance of athletes remains a challenge in movement science and sports psychology. Non-invasive quantitative electroencephalography (QEEG) recordings can be used to analyze various factors in sports psychology. METHOD: In this context, sports-related psychological factors were used to estimate the performance of Thai professional female soccer players before a competition. The QEEG recordings of thirty-two players were recorded three times: twice before a competition (once a week) and a week after a competition. Four factors of sports psychology were estimated and observed: anxiety, perceptual response to an acute bout of brain activity, assertiveness, and brain central fatigue. A brain topographic map (absolute power) and brain connectivity (coherence and amplitude asymmetry) data were used to analyze sports-related psychological factors. These factors were measurable based on the brain activity of the athletes and could be used to evaluate their performance during competitions by using QEEG values. RESULTS: Sports-related psychological performance was estimated by Pearson's correlation coefficients, which revealed that a quick perceptual response to an acute bout of brain activity could predict an athlete's performance during competition (r = .584, p = .000). Additionally, Spearman's correlation coefficients were used to estimate athletes performance. The results revealed a strong relationship ( r s =.634, p = .000), which was derived from the summation of anxiety and perceptual response to an acute bout of brain activity. CONCLUSION: Consequently, the results of the present study can provide information to help staff coaches to choose the best performing players, representing an alternative method for accurately selecting key players in the competitive sports community.

Exploration of illusory visual motion stimuli: An EEG-based brain-computer interface for practical assistive communication systems.

This paper presents an illusory visual motion stimulus-based brain-computer interface (BCI). We aim to use the proposed system to enhance the motor imagery (MI) modality. Since motor imagery requires a long time for training, a stimulation method with external stimuli through the sensory system is an alternative method for increasing efficiency. The research is divided into two parts. First, we observed the visual motion illusion pattern based on brain topographic maps for the novel BCI modality. Second, we implemented the illusory visual motion stimulus-based BCI system. Arrow and moving-arrow patterns were used to modulate alpha rhythms at the visual and motor cortex. The arrow pattern had an average classification accuracy of approximately 78.5%. Additionally, illusory visual motion stimulus-based BCI systems are proposed using the proposed feature extraction and decision-making algorithm. This proposed BCI system can control the cursor moving in the left or right direction with the designed algorithm to create five commands for assistive communication. Ten volunteers participated in the experiment, and a brain-computer interface system with motor imagery and an illusory visual motion stimulus were used to compare efficiencies. The results showed that the proposed method achieved approximately 4% higher accuracy than motor imagery. The accuracy of the proposed illusory visual motion stimulus and algorithm was approximately 80.3%. Therefore, an illusory visual motion stimulus hybrid BCI system can be incorporated into the MI-based BCI system for beginner motor imagery. Based on the results, the proposed assistive communication system can be used to enhance communication in people with severe disabilities.

EEG Delta/Theta Ratio and Microstate Analysis Originating Novel Biomarkers for Malnutrition-Inflammation Complex Syndrome in ESRD Patients.

The Malnutrition-Inflammation Score (MIS) was initially proposed to evaluate malnutrition-inflammation complex syndrome (MICS) in end-stage renal disease (ESRD) patients. Although MICS should be routinely evaluated to reduce the hospitalization and mortality rate of ESRD patients, the inconvenience of the MIS might limit its use. Cerebral complications in ESRD, possibly induced by MICS, were previously assessed by using spectral electroencephalography (EEG) via the delta/theta ratio and microstate analysis. Correspondingly, EEG could be used to directly assess MICS in ESRD patients, but the relationships among MICS and these EEG features remain inconclusive. Thus, we aimed to investigate the delta/theta ratio and microstates in ESRD patients with high and low risks of MICS. We also attempted to identify the correlation among the MIS, delta/theta ratio, and microstate parameters, which might clarify their relationships. To achieve these objectives, a total of forty-six ESRD subjects were willingly recruited. We collected their blood samples, MIS, and EEGs after receiving written informed consent. Sixteen women and seven men were allocated to low risk group (MIS ≤ 5, age 57.57 ± 14.88 years). Additionally, high risk group contains 15 women and 8 men (MIS > 5, age 59.13 ± 11.77 years). Here, we discovered that delta/theta ratio (p < 0.041) and most microstate parameters (p < 0.001) were significantly different between subject groups. We also found that the delta/theta ratio was not correlated with MIS but was strongly with the average microstate duration (ρ = 0.708, p < 0.001); hence, we suggested that the average microstate duration might serve as an alternative encephalopathy biomarker. Coincidentally, we noticed positive correlations for most parameters of microstates A and B (0.54 ≤ ρ ≤ 0.68, p < 0.001) and stronger negative correlations for all microstate C parameters (-0.75 ≤ ρ ≤ -0.61, p < 0.001). These findings unveiled a novel EEG biomarker, the MIC index, that could efficiently distinguish ESRD patients at high and low risk of MICS when utilized as a feature in a binary logistic regression model (accuracy of train-test split validation = 1.00). We expected that the average microstate duration and MIC index might potentially contribute to monitor ESRD patients in the future.

Modified-Distribution Entropy as the Features for the Detection of Epileptic Seizures.

Epilepsy is one of the most common chronic neurological disorders, and therefore, diagnosis and treatment methods are urgently needed for these patients. Many methods and algorithms that can detect seizures in epileptic patients have been proposed. Electroencephalogram (EEG) is one of helpful tools for investigating epilepsy forms in patients, however, an expert in the neurological field must perform a visual inspection to identify a seizure. Such analyses require longer time because of the huge dataset recorded from many electrodes which are put on the human scalp. With the non-stationary nature of EEG, especially during the abnormality periods, entropy measures gain more interest in the field. In this work, by exploring the advantages of both reliable state-of-the-art entropies, fuzzy entropy and distribution entropy, a modified-Distribution entropy (mDistEn) for epilepsy detection is proposed. As the results, the proposed mDistEn method can successfully achieve the same consistency and better accuracy than using the state-of-the-art entropies. The mDistEn corresponds to higher Area Under the Curve (AUC) values compared with the fuzzy entropy and the distribution entropy and yields 92% classification accuracy.