Power In Frequency Bands Research Articles

Obsessive-compulsive disorder (OCD) is associated with increased electroencephalographic (EEG) delta and theta oscillatory power but reduced delta connectivity

Obsessive-Compulsive Disorder (OCD) is a mental health condition causing significant decline in the quality of life of sufferers and the limited knowledge on the pathophysiology hinders successful treatment. The aim of the current study was to examine electroencephalographic (EEG) findings of OCD to broaden our understanding of the disease. Resting-state eyes-closed EEG data was recorded from 25 individuals with OCD and 27 healthy controls (HC). The 1/f arrhythmic activity was removed prior to computing oscillatory powers of all frequency bands (delta, theta, alpha, beta, gamma). Cluster-based permutation was used for between-group statistical analyses, and comparisons were performed for the 1/f slope and intercept parameters. Functional connectivity (FC) was measured using coherence and debiased weighted phase lag index (d-wPLI), and statistically analyzed using the Network Based Statistic method. Compared to HC, the OCD group showed increased oscillatory power in the delta and theta bands in the fronto-temporal and parietal brain regions. However, there were no significant between-group findings in other bands or 1/f parameters. The coherence measure showed significantly reduced FC in the delta band in OCD compared to HC but the d-wPLI analysis showed no significant differences. OCD is associated with raised oscillatory power in slow frequency bands in the fronto-temporal brain regions, which agrees with the previous literature and therefore is a potential biomarker. Although delta coherence was found to be lower in OCD, due to inconsistencies found between measures and the previous literature, further research is required to ascertain definitive conclusions.

Correlation between electroencephalographic markers in the healthy brain

Mental disorders have an increasing tendency and represent the main burden of disease to society today. A wide variety of electroencephalographic (EEG) markers have been successfully used to assess different symptoms of mental disorders. Different EEG markers have demonstrated similar classification accuracy, raising a question of their independence. The current study is aimed to investigate the hypotheses that different EEG markers reveal partly the same EEG features reflecting brain functioning and therefore provide overlapping information. The assessment of the correlations between EEG signal frequency band power, dynamics, and functional connectivity markers demonstrates that a statistically significant correlation is evident in 37 of 66 (56%) comparisons performed between 12 markers of different natures. A significant correlation between the majority of the markers supports the similarity of information in the markers. The results of the performed study confirm the hypotheses that different EEG markers reflect partly the same features in brain functioning. Higuchi’s fractal dimension has demonstrated a significant correlation with the 82% of other markers and is suggested to reveal a wide spectrum of various brain disorders. This marker is preferable in the early detection of symptoms of mental disorders.

Gamification of an n-back working memory task – Is it worth the effort? An EEG and eye-tracking study

Gamification of cognitive tasks might positively affect emotional-motivational factors (emotional design perspective) or negatively affect cognitive factors like working memory load (minimalistic design perspective). The current study examined the effects of gamification in a spatial n-back working memory task on task performance, task load (i.e., working memory load and effort), and subjective task experience. Task load was assessed by the physiological process measures pupil dilation and EEG theta (4–6 Hz) and alpha (8–13 Hz) frequency band power. Gamification was achieved by elements of emotional design (i.e., the visual screen design using, e.g., color, cartoon figures as n-back stimuli, and a narrative embedding of the task). While EEG and eye-tracking were recorded, participants conducted gamified and non-gamified 1-back and 2-back load levels. The gamification resulted in positive effects on subjective task experience and affect. Despite these effects, gamification did not affect task performance and task load. However, exploratory analyses revealed increased EEG theta power at right-parietal electrodes for gamified task versions compared to non-gamified ones. Potentially, this effect might indicate participants' increased effort or concentration in the gamified n-back task. In line with an emotional design perspective, gamification positively altered subjective task experience and affect without hampering task performance and therefore justify the extra effort of implementing game elements.

Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review.

Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.

Transcranial Direct Current Stimulation Modulates Working Memory Maintenance Processes in Healthy Individuals.

The effects of transcranial direct current stimulation (tDCS) at the pFC are often investigated using cognitive paradigms, particularly working memory tasks. However, the neural basis for the neuromodulatory cognitive effects of tDCS, including which subprocesses are affected by stimulation, is not completely understood. We investigated the effects of tDCS on working memory task-related spectral activity during and after tDCS to gain better insights into the neurophysiological changes associated with stimulation. We reanalyzed data from 100 healthy participants grouped by allocation to receive either sham (0 mA, 0.016 mA, and 0.034 mA) or active (1 mA or 2 mA) stimulation during a 3-back task. EEG data were used to analyze event-related spectral power in frequency bands associated with working memory performance. Frontal theta event-related synchronization (ERS) was significantly reduced post-tDCS in the active group. Participants receiving active tDCS had slower RTs following tDCS compared with sham, suggesting interference with practice effects associated with task repetition. Theta ERS was not significantly correlated with RTs or accuracy. tDCS reduced frontal theta ERS poststimulation, suggesting a selective disruption to working memory cognitive control and maintenance processes. These findings suggest that tDCS selectively affects specific subprocesses during working memory, which may explain heterogenous behavioral effects.

Alternations of neuromagnetic activity across neurocognitive core networks among benign childhood epilepsy with centrotemporal spikes: A multi-frequency MEG study.

We aimed to investigate the alternations of neuromagnetic activity across neurocognitive core networks among early untreated children having benign childhood epilepsy with centrotemporal spikes (BECTS). We recorded the Magnetoencephalography (MEG) resting-state data from 48 untreated children having BECTS and 24 healthy children. The fourth edition of the Wechsler Intelligence Scale for Children (WISC-IV) was utilized to divide the children with BECTS into two groups: the cognitive impairment (CI) group with a full-scale intelligence quotient (FSIQ) of < 90 and the cognitive non-impairment (CNI) group with an FSIQ of > 90. We selected 26 bilateral cognitive-related regions of interest based on the triple network model. The neurocognitive core network spectral power was estimated using a minimum norm estimate (MNE). In the CNI group, the spectral power inside the bilateral anterior cingulate cortex (ACC) and the bilateral caudal middle frontal cortex (CMF) enhanced within the delta band and reduced within the alpha band. Both the CI and the CNI group demonstrated enhanced spectral power inside the bilateral posterior cingulate cortex (PCC), bilateral precuneus (PCu) region, bilateral superior and middle temporal cortex, bilateral inferior parietal lobe (IPL), and bilateral supramarginal cortex (SM) region in the delta band. Moreover, there was decreased spectral power in the alpha band. In addition, there were consistent changes in the high-frequency spectrum (> 90 Hz). The spectral power density within the insula cortex (IC), superior temporal cortex (ST), middle temporal cortex (MT), and parahippocampal cortex (PaH) also decreased. Therefore, studying high-frequency activity could lead to a new understanding of the pathogenesis of BECTS. The alternations of spectral power among neurocognitive core networks could account for CI among early untreated children having BECTS. The dynamic properties of spectral power in different frequency bands could behave as biomarkers for diagnosing new BECTS.

The effect of musk incense stick aroma inhalation on different features of electroencephalogram signals and working memory for use in neurofeedback training

Susceptibility of brain activity to the inhalation of different aromas established the idea of inhaling an aroma as an auxiliary factor in neurofeedback training. The present study has focused on investigating the effect of musk incense stick aroma inhalation on various features of EEG signals (power of different frequency bands along with Katz fractal dimension (Katz’s FD), approximate entropy (ApEn) and scaling exponent) and working memory for use in neurofeedback training. To raise this aim, the EEG signals were recorded under eyes-open (EO) and eyes-closed (EC) conditions for 22 healthy subjects before and during the musk incense stick aroma inhalation, followed by performing Wechsler Memory Scale (WMS). The results showed that musk incense stick aroma inhalation under EO condition increased the relative power of delta, theta, sensorimotor rhythm (SMR) and low beta frequency bands along with ApEn and Katz’s FD in most channels, whereas decreased the relative power of alpha, high beta and gamma bands. A decrease was found in the relative power of delta, theta, SMR, low beta, high beta and gamma frequency bands in some channels under EC conditions. ApEn and Katz’s FD values also increased in most channels under EC conditions following musk incense stick aroma inhalation. The scaling exponent also decreased under the same conditions in most channels. However, these changes were not significant in all channels. The WMS score also increased after inhaling aroma, but not significantly. The findings of this research suggest the use of musk incense stick aroma inhalation during neurofeedback training to increase training efficiency.

Driver Attention Assessment Using Physiological Measures from EEG, ECG, and EDA Signals.

In this paper, we consider the evaluation of the mental attention state of individuals driving in a simulated environment. We tested a pool of subjects while driving on a highway and trying to overcome various obstacles placed along the course in both manual and autonomous driving scenarios. Most systems described in the literature use cameras to evaluate features such as blink rate and gaze direction. In this study, we instead analyse the subjects' Electrodermal activity (EDA) Skin Potential Response (SPR), their Electrocardiogram (ECG), and their Electroencephalogram (EEG). From these signals we extract a number of physiological measures, including eye blink rate and beta frequency band power from EEG, heart rate from ECG, and SPR features, then investigate their capability to assess the mental state and engagement level of the test subjects. In particular, and as confirmed by statistical tests, the signals reveal that in the manual scenario the subjects experienced a more challenged mental state and paid higher attention to driving tasks compared to the autonomous scenario. A different experiment in which subjects drove in three different setups, i.e., a manual driving scenario and two autonomous driving scenarios characterized by different vehicle settings, confirmed that manual driving is more mentally demanding than autonomous driving. Therefore, we can conclude that the proposed approach is an appropriate way to monitor driver attention.

Abstract WP39: Subhairline Electroencephalography For Detection Of Large Vessel Occlusion Stroke

Introduction: Endovascular thrombectomy (EVT) for large vessel occlusion stroke of the anterior circulation (LVO-a) can only be performed in selected hospitals. A prehospital triage instrument with which paramedics can reliably identify patients with an LVO-a stroke would enable direct routing of these patients to the right hospital and would consequently improve patient outcome. Electroencephalography (EEG) has previously shown to discriminate between patients with and without an LVO-a stroke. In this study, the diagnostic accuracy of subhairline EEG for LVO-a stroke was evaluated. Methods: A 3-minute EEG recording was performed in 35 patients who were presented to the emergency room with a suspected stroke or known LVO-a stroke, as soon as possible after presentation, and prior to potential EVT. Recordings were performed using 9 self-adhesive electrodes placed on the forehead and behind the ears. We evaluated the diagnostic accuracies of EEG features quantifying frequency band power (relative delta, theta, alpha and lower beta powers and power ratios), brain symmetry (pairwise derived Brain Symmetry Index [pdBSI]) and connectivity (Magnitude Squared Coherence [MSC] and Weighted Phase Lag Index) for LVO-a stroke using receiver operating characteristic analysis. Optimal cut-off points were determined as the maximum sensitivity at a specificity of ≥ 80% for LVO-a stroke. Results: Median age was 75 (IQR 67-81) years and 20/35 (57%) patients were male. 13/35 (37%) patients had an LVO-a stroke, 12/35 (34%) a non-LVO-a ischemic stroke, 4/35 (11%) a transient ischemic attack and 6/35 (17%) a stroke mimic. Median onset-to-EEG-time was 250 (IQR 118-535) minutes. Recordings of all patients were of sufficient quality to calculate frequency band power and brain symmetry measures. The connectivity measures could not be calculated for 3 LVO-a stroke patients. The highest diagnostic accuracy for LVO-a stroke was reached by the MSC in the delta frequency band (AUC 0.80; sensitivity 80%; specificity 82%) and the pdBSI in the theta frequency band (AUC 0.78; sensitivity 77%; specificity 82%). Conclusions: Subhairline EEG is a promising instrument for detection of LVO-a stroke, but validation in a larger study population and in the prehospital setting is necessary.

Heart rate variability and microvolt Twave alternans changes during ajmaline test may predict prognosis in Brugada syndrome.

Drug-induced typeI Brugada syndrome (BrS) is associated with a ventricular arrhythmia (VA) rate of 1 case per 100 person-years. This study aims to evaluate changes in electrocardiographic (ECG) parameters such as microvolt Twave alternans (mTWA) and heart rate variability (HRV) at baseline and during ajmaline testing for BrS diagnosis. Consecutive patients diagnosed with BrS during ajmaline testing with 5-year follow-up were included in this study. For comparison, a negative ajmaline control group and an isoproterenol control group were also included. ECG recordings during ajmaline or isoproterenol test were divided in two timeframes from which ECG parameters were calculated: a 5-min baseline timeframe and a 5-min drug timeframe. A total of 308 patients with BrS were included, 22 (0.7%) of which suffered VAs during follow-up. One hundred patients were included in both isoproterenol and negative ajmaline control groups. At baseline, there was no difference in ECG parameters between control groups and patients with BrS, nor between BrS with and without VAs. During ajmaline testing, BrS with VAs presented longer QRS duration [159 ± 34ms versus 138 (122-155) ms, p = 0.006], higher maximum mTWA [33.8 (14.0-114) µV versus 8.00 (3.67-28.2)µV, p = 0.001], and lower power in low frequency band [25.6 (5.8-53.8) ms2 versus 129.5 (52.7-286) ms2, p < 0.0001] when compared to BrS without VAs. Ajmaline induced important HRV changes similar to those observed during isoproterenol. Increased mTWA was observed only in patients with BrS. BrS with VAs during follow-up presented worse changes during ajmaline test, including lower LF power and higher maximum mTWA which were independent predictors of events.

Sleep Continuity, Stability and Cyclic Organization Are Impaired in Insomniacs: A Case-Control Study.

The possibility of distinguishing insomniacs from good sleepers based on polysomnography (PSG) remains an open question. While these groups show modest differences in traditional PSG parameters, some studies suggest that finer measures may be more useful. Here we assess differences between good sleepers (GS), poor sleepers (PS) and insomniacs (IN) in classical PSG measures as well as in sleep continuity, stability and cyclic organization. PSG-monitored sleep (two nights) of 17 IN (diagnosed through a standard clinical interview; Pittsburgh Sleep Quality Index (PSQI) ≥ 5, Insomnia Severity Index (ISI) &gt; 14) was compared to that of 33 GS (PSQI &lt; 5) and 20 PS (PSQI ≥ 5, ISI ≤ 14). Compared to GS, IN were impaired in sleep macrostructure (sleep latency, sleep efficiency, WASO%) and in continuity, stability and organization, whereas PS only showed disrupted continuity and stability. Spindle parameters were comparable between IN and GS, but the former displayed enhanced power in fast frequency bands. Our findings support the hypothesis of a continuum between individuals with self-reported poor sleep and insomniacs. Further, they add to extant data on impaired sleep continuity, stability and organization in poor sleepers and elderly individuals, underlining the utility of including these measures in standard sleep assessments.

ЭКСПЕРИМЕНТАЛЬНОЕ ИССЛЕДОВАНИЕ ВЕГЕТАТИВНОЙ НЕРВНОЙ СИСТЕМЫ ПОДРОСТКОВ С РАЗНОЙ СТЕПЕНЬЮ ВОВЛЕЧЕННОСТИ В ЦИФРОВУЮ СРЕДУ

The modern literature records a high relevance of diagnosing the deterioration of the psychological health of the younger generation in conditions of accelerated digitalization, determined by psycho-emotional stress and is associated with the functions of the autonomic nervous system (ANS), the role of which has not been studied either in the formation of over-involvement with the Internet, or in determining the success of the development of educational programs by modern youth. One of the most important markers for assessing the activity of the ANS is the heart rate and its variability. Thus, the purpose of our work can be defined as the study of the relationship of heart rate parameters (R-R intervals) with the characteristics of digital preferences of adolescents. It is assumed that the choice of activities on the Internet, as well as the time spent on leisure, have certain connections with the characteristics of R-R intervals and its variability, with the predominance of activity of the parasympathetic or sympathetic links of the ANS. The study was carried out during the COVID-19 pandemic in 2021, which aggravated the psycho-emotional state of students. The author’s questionnaire “Digital preferences of modern teenagers” was used in the work, which had previously been tested on schoolchildren in Moscow. In a separate experiment on a part of the total sample of students (n = 24), a R-R intervals was recorded using a certified ECG device of the APK “Veda-pulse”. Registration was carried out individually with each subject, on average, ECG registration lasted 5 minutes. The characteristics of the behavior of the surveyed group of St. Petersburg schoolchildren have a high degree of similarity with all other surveyed groups of peers in Moscow and Lipetsk. The frequency-spectral parameters of the R-R- interval’s variability in a separate experiment are characterized by high variety, the maximum group variety is shown for the low frequency band (LF) of the spectrum of R-R intervals. A highly reliable positive relationship of priority interests on the Internet with the power of the spectrum of all frequency bands and with the gender of the subjects was found. Students' academic performance correlates positively and with highly reliably with the power of the super low frequency band (VLF) and negatively with the power of the low frequency band (LF). The results are discussed in the context of the interrelationships of students' academic performance, gender and interests on the Internet with the activity of their sympathetic and parasympathetic components of the ANS and the central link of heart rate regulation.

Towards emotionally intelligent buildings: A Convolutional neural network based approach to classify human emotional experience in virtual built environments

Accurate classification of human emotions in designed spaces is essential for architects and engineers, who aim to maximize positive emotions by configuring architectural design features. Previous studies at the conjunction of neuroscience and architecture confirmed the impact of architectural design features on human emotions. Recent development of biometric sensors enabled researchers to identify emotions by measuring human physiological responses (e.g., the use of electroencephalogram (EEG) to measure brain activities). However, a gap in the knowledge exists in terms of an accurate classification model for human emotions in design variants. This study proposed a convolutional neural network (CNN) based approach to classify human emotions. The approach considered two types of CNN architectures as CNN ensemble and auto-encoders. The inputs of these CNN algorithms were 2D images generated by projecting the frequency band power of EEG onto the scalp graph in accordance with the electrode placements. This transformation from time-series EEG data to 2D frequency band power images retain the spatial, time and frequency domain features from participants’ brain dynamics. Performance of the proposed approach was validated using multiple metrics, including precision, recall, f-1 score, and Area Under Curve (AUC). Results showed that the auto-encoder based approach achieved the best performance with an AUC of 0.95.

The Effect of Lifting-and-Thrusting Laser Acupuncture on Electrodermal Activity of Acupoints, Pulse Characteristics, and Brainwave.

Acupuncture has been shown as an effective traditional Chinese medicine treatment method, especially for pain relief. Recently, laser acupuncture is becoming increasingly popular, thanks to its noninvasive and painless nature and effectiveness in treating diseases, proven by many studies (for example, some previous studies showed that low-power laser stimulation is able to increase the power of alpha rhythms and theta waves). In our prior work, we developed a novel laser acupuncture model that emulates lifting-and-thrusting operation commonly used in traditional needle acupuncture and showed its benefit in improving cardiac output and peripheral circulation. By extending our previous studies, in this work, we perform extensive experiments to understand the effect of such a system on electrodermal activity (EDA) of acupoints, pulse characteristics, and brainwave, to further verify its efficacy. In particular, we found that laser stimulation could cause significant changes in EDA of acupoints, pulse amplitude, pulse-rate-variability (PRV), and acupoint conductance, as a function of laser power and stimulation time. In addition, laser acupuncture with the lifting-and-thrusting operation has more significant effect on increasing the power of alpha and theta frequency bands as compared to laser acupuncture without the lifting-and-thrusting operation. Finally, given sufficient stimulation time (e.g., > 20 min), the performance of a low-powered laser acupuncture with the lifting-and-thrusting operation could be comparable to that of traditional needle acupuncture.

Preservation of EEG spectral power features during simultaneous EEG-fMRI.

Electroencephalographic (EEG) data quality is severely compromised when recorded inside the magnetic resonance (MR) environment. Here we characterized the impact of the ballistocardiographic (BCG) artifact on resting-state EEG spectral properties and compared the effectiveness of seven common BCG correction methods to preserve EEG spectral features. We also assessed if these methods retained posterior alpha power reactivity to an eyes closure-opening (EC-EO) task and compared the results from EEG-informed fMRI analysis using different BCG correction approaches. Electroencephalographic data from 20 healthy young adults were recorded outside the MR environment and during simultaneous fMRI acquisition. The gradient artifact was effectively removed from EEG-fMRI acquisitions using Average Artifact Subtraction (AAS). The BCG artifact was corrected with seven methods: AAS, Optimal Basis Set (OBS), Independent Component Analysis (ICA), OBS followed by ICA, AAS followed by ICA, PROJIC-AAS and PROJIC-OBS. EEG signal preservation was assessed by comparing the spectral power of traditional frequency bands from the corrected rs-EEG-fMRI data with the data recorded outside the scanner. We then assessed the preservation of posterior alpha functional reactivity by computing the ratio between the EC and EO conditions during the EC-EO task. EEG-informed fMRI analysis of the EC-EO task was performed using alpha power-derived BOLD signal predictors obtained from the EEG signals corrected with different methods. The BCG artifact caused significant distortions (increased absolute power, altered relative power) across all frequency bands. Artifact residuals/signal losses were present after applying all correction methods. The EEG reactivity to the EC-EO task was better preserved with ICA-based correction approaches, particularly when using ICA feature extraction to isolate alpha power fluctuations, which allowed to accurately predict hemodynamic signal fluctuations during the EEG-informed fMRI analysis. Current software solutions for the BCG artifact problem offer limited efficiency to preserve the EEG spectral power properties using this particular EEG setup. The state-of-the-art approaches tested here can be further refined and should be combined with hardware implementations to better preserve EEG signal properties during simultaneous EEG-fMRI. Existing and novel BCG artifact correction methods should be validated by evaluating signal preservation of both ERPs and spontaneous EEG spectral power.

Normative Values for Body Surface Gastric Mapping Evaluations of Gastric Motility Using Gastric Alimetry: Spectral Analysis.

Body surface gastric mapping (BSGM) is a new noninvasive test of gastric function. BSGM offers several novel and improved biomarkers of gastric function capable of differentiating patients with overlapping symptom profiles. The aim of this study was to define normative reference intervals for BSGM spectral metrics in a population of healthy controls. BSGM was performed in healthy controls using Gastric Alimetry (Alimetry, New Zealand) comprising a stretchable high-resolution array (8 × 8 electrodes; 196 cm 2 ), wearable Reader, and validated symptom-logging App. The evaluation encompassed a fasting baseline (30 minutes), 482 kCal meal, and 4-hour postprandial recording. Normative reference intervals were calculated for BSGM metrics including the Principal Gastric Frequency, Gastric Alimetry Rhythm Index (a measure of the concentration of power in the gastric frequency band over time), body mass index (BMI)-adjusted amplitude (μV), and fed:fasted amplitude ratio. Data were reported as median and reference interval (5th and/or 95th percentiles). A total of 110 subjects (55% female, median age 32 years [interquartile range 24-50], median BMI 23.8 kg/m 2 [interquartile range 21.4-26.9]) were included. The median Principal Gastric Frequency was 3.04 cycles per minute; reference interval: 2.65-3.35 cycles per minute. The median Gastric Alimetry Rhythm Index was 0.50; reference interval: ≥0.25. The median BMI-adjusted amplitude was 37.6 μV; reference interval: 20-70 μV. The median fed:fasted amplitude ratio was 1.85; reference interval ≥1.08. A higher BMI was associated with a shorter meal-response duration ( P = 0.014). This study provides normative reference intervals for BSGM spectral data to inform diagnostic interpretations of abnormal gastric function.

Predicting collision cases at unsignalized intersections using EEG metrics and driving simulator platform

Unsignalized intersection collision has been one of the most dangerous accidents in the world. How to identify road hazards and predict the potential intersection collision ahead are challenging problems in traffic safety. This paper studies the feasibility of EEG metrics to forecast road hazards and presents an improved neural network model to predict intersection collision based on EEG metrics and driving behavior. It is demonstrated that EEG metrics show significant differences between collision and non-collision cases. It indicates that EEG metrics can serve as effective indicators to predict the collision probability. The drivers with higher relative power in fast frequency band (alpha and beta), lower relative power in slow frequency band (delta and theta) are more likely to have conflicts. The prediction using three machine learning models (Multi-layer perceptron (MLP), Logistic regression (LR) and Random forest (RF)) based on three input datasets (only EEG metrics, only driving behavior and combined EEG metrics with driving behavior) are compared. The results show that for single time point prediction, MLP model has the highest accuracy among three machine learning models. The model solely based on EEG metrics datasets has higher accuracy than driving behavior as well as combined datasets. However, for multi-time point prediction, the accuracy of MLP is only 73.9%, worse than LR and RF. We improved the MLP model by adding attention mechanism layer and using random forest model to select important features. As a consequence, the accuracy is greatly improved and reaches 88%. This study demonstrates the importance and feasibility of EEG signals to identify unsafe drivers ahead. The improved neural network model can be helpful to reduce intersection accidents and improve traffic safety.

Resting-state Quantitative EEG Spectral Patterns in Migraine During Ictal Phase Reveal Deviant Brain Oscillations: Potential Role of Density Spectral Array.

Background. Migraine headache may have a substantial bearing on the brain functions and rhythms. Electrophysiological methods can detect changes in brain oscillation. The present work examined the frequency band power through quantitative electroencephalogram (qEEG) and density spectral array (DSA) to elucidate the resting state neuronal oscillations in migraine. Methods. Clinical details were inquired, and EEG was recorded in migraineurs and healthy controls. The acquired data were analyzed to determine power spectral density values and obtain DSA graphs. The absolute and relative powers for the alpha, theta, and delta frequencies in frontocentral, parieto-occipital, and temporal regions were determined. A correlation of significant EEG findings with clinical features of migraine was sought. Results. Forty-five participants were enrolled in the study. The spectrum analysis revealed an increase in the relative theta power (P < .001) and a reduction in relative alpha power (P < .001) in the observed cortical areas among the migraineurs as compared to the healthy controls. Relative delta power was increased over the frontocentral region (P = .001), slightly more on the symptomatic side of the head. In addition, frontocentral delta power had a moderate positive correlation (r = .697, n = 22, P = .000) with migraine severity. Conclusion. The study supports the evidence of a neuronal dysfunction existing in the resting state during the ictal phase of migraine. qEEG can reveal these aberrant oscillations. Utility of DSA to depict the changes in brain activity in migraine is a potential area for research. The information can help formulate new therapeutic strategies towards alteration in cortical excitability using brain stimulation techniques.

Hemispheric Utilization of Alpha Oscillatory Dynamics as a Unique Biomarker of Neural Compensation in Females with Fragile X Syndrome.

Fragile X syndrome (FXS) is a neurodevelopmental disorder caused by a trinucleotide expansion on the FMR1 gene and characterized by intellectual disability, sensory hypersensitivity, executive function difficulties, and social anxiety. Recently, efforts to define neural biomarkers for FXS have highlighted disruptions to power in the alpha frequency band; however the dynamic mechanisms supporting these findings are poorly understood. The current study aimed to explore the temporal and hemispheric dynamics supporting alpha phenotypes in FXS and their relationship with neural phenotypes related to auditory processing using electroencephalography during an auditory evoked task. Adolescents and adults (N = 36) with FXS and age/sex matched typically developing controls (N = 40) completed an auditory chirp task. Frontal alpha power in the prestimulus period was decomposed into "bursts" using percentile thresholding, then assessed for number of bursts per second (burst count) and burst length. Data were compared across left and right hemispheres to assess lateralization of neural activity. Individuals with FXS showed more differences in alpha power compared to TDC primarily in the right hemisphere. Notably, alpha hemisphere outcomes in males with FXS were driven by the number of times they entered a dynamically relevant period of alpha (burst count) rather than length of time spent in alpha. Females with FXS showed reduced burst counts but remained in sustained high alpha states for longer periods of time. Length of time spent in alpha may reflect a modulatory or compensatory mechanism capable of recovering sensory processing abilities in females with FXS resulting in a less severe clinical presentation. Right hemisphere abnormalities may impact sensory processing differences between males and females with FXS. The relationship between alpha burst length, count, sex, and hemisphere may shed light on underlying mechanisms for previously observed alpha power abnormalities in FXS and their variation by sex.

O072 Episodes of drowsiness present during cognitively demanding tasks

Abstract Introduction Insufficient sleep is associated with impaired performance in cognitive tasks and executive functioning. Adolescence is a time of increasing autonomy and lifestyle changes, where social, educational, and work demands often impact on sleep opportunity. The impact of habitual short sleep opportunity on cognitive functioning is poorly understood. We examined the difference in EEG-based alertness during low and high load cognitive tasks in sleep restricted young adults. Methods Ninety-nine habitually sleep restricted young adults (M=20.65, SD=1.91, 73% female) participated in the study. EEG-based (F4-M1, O2-M1) alertness was examined under two conditions: (1) low load; a simulated slow drive and (2) high load; a weekly activity mapping task. A novel model predicted alertness as a continuous function of EEG power in beta, alpha, theta, and delta frequency bands. Model output 1 indicates alertness (i.e., wake), while values approaching zero indicate reductions in alertness. Statistical significance was assessed by two-tailed t-test (unequal variance), and effect size reported by Cohen’s D. Results Mean model output values were significantly (p&amp;lt;0.0001) lower during the driving task (0.16, 95%CI=0.12-0.19) compared to the mapping task (0.92, 0.87-0.97), with substantial effect size (D=3.9). Conclusions This study demonstrates that sleep restricted young adults are less alert during tasks with low cognitive demands, even though the task we tested (simulated drive) represents high real-world risk. In contrast, these individuals show elevated levels of alertness during cognitively demanding tasks despite absence of real-world risk. Further study investigating potential mitigating effects through sleep modulation may reveal simple health recommendations