Effect Of Electroencephalography Research Articles

Simultaneous EEG-fMRI Investigation of Rhythm-Dependent Thalamo-Cortical Circuits Alteration in Schizophrenia.

Schizophrenia is accompanied by aberrant interactions of intrinsic brain networks. However, the modulatory effect of electroencephalography (EEG) rhythms on the functional connectivity (FC) in schizophrenia remains unclear. This study aims to provide new insight into network communication in schizophrenia by integrating FC and EEG rhythm information. After collecting simultaneous resting-state EEG-functional magnetic resonance imaging data, the effect of rhythm modulations on FC was explored using what we term "dynamic rhythm information." We also investigated the synergistic relationships among three networks under rhythm modulation conditions, where this relationship presents the coupling between two brain networks with other networks as the center by the rhythm modulation. This study found FC between the thalamus and cortical network regions was rhythm-specific. Further, the effects of the thalamus on the default mode network (DMN) and salience network (SN) were less similar under alpha rhythm modulation in schizophrenia patients than in controls ([Formula: see text]). However, the similarity between the effects of the central executive network (CEN) on the DMN and SN under gamma modulation was greater ([Formula: see text]), and the degree of coupling was negatively correlated with the duration of disease ([Formula: see text], [Formula: see text]). Moreover, schizophrenia patients exhibited less coupling with the thalamus as the center and greater coupling with the CEN as the center. These results indicate that modulations in dynamic rhythms might contribute to the disordered functional interactions seen in schizophrenia.

Longitudinal Stability of Neural Correlates of Pediatric Attention Deficit Hyperactivity Disorder: A Pilot Study of Event Related Potentials and Electroencephalography

Objective: Stability and developmental effects of electroencephalography (EEG) and event related potential (ERP) correlates of ADHD are understudied. This pilot study examined stability and developmental changes in ERP and EEG metrics of interest. Methods: Thirty-seven 7 to 11-year-old children with ADHD and 15 typically developing (TD) children completed EEG twice, 11 to 36 months apart. A series of mixed effects linear models were run to examine stability and developmental effects of EEG and ERP metrics. Results: Stability and developmental effects of EEG and ERP correlates of ADHD varied considerably across metrics. P3 amplitude was stable over time and showed diverging developmental trajectories across groups. Developmental differences were apparent in error related ERPs and resting aperiodic exponent. Theta-beta ratio was stable over time among all children. Conclusions: Developmental trajectories of EEG and ERP correlates of ADHD are candidate diagnostic markers. Replication with larger samples is needed.

Combined behavioral and EEG evidence for the 70 Hz frequency selection of short-term spinal cord stimulation in disorders of consciousness.

This study investigated the prognostic effect of electroencephalography (EEG) instant effects of single spinal cord stimulation (SCS) on clinical outcome in disorders of consciousness (DOC) and the time-dependent brain response during the recovery of consciousness prompted by SCS. Twenty three patients with DOC underwent short-term SCS (stSCS) implantation operation. Then, all patients received the postoperative EEG test including EEG record before (T1) and after (T2) single SCS session. Subsequently, 2 weeks stSCS treatment was performed and revised coma recovery scale (CRS-R) and EEG data were collected. Finally, they were classified into effective and ineffective groups at 3-month follow-up (T6). The parietal-occipital (PO) connectivity and clustering coefficients (CC) in the beta band of the effective group at the 1 week after the treatment (T5) were found to be higher than preoperative assessment (T0). Correlation analysis showed that the change in beta CC at T1/T2was correlated with the change in CRS-R at T0/T6. In addition, the change in PO connectivity and CC in the beta at T0/T5 were also correlated with the change in CRS-R at T0/T5. SCS may facilitate the recovery of consciousness by enhancing local information interaction in posterior brain regions. And the recovery can be predicted by beta CC in the EEG test.

Detection of Pilot's Mental Workload Using a Wireless EEG Headset in Airfield Traffic Pattern Tasks.

Elevated mental workload (MWL) experienced by pilots can result in increased reaction times or incorrect actions, potentially compromising flight safety. This study aims to develop a functional system to assist administrators in identifying and detecting pilots' real-time MWL and evaluate its effectiveness using designed airfield traffic pattern tasks within a realistic flight simulator. The perceived MWL in various situations was assessed and labeled using NASA Task Load Index (NASA-TLX) scores. Physiological features were then extracted using a fast Fourier transformation with 2-s sliding time windows. Feature selection was conducted by comparing the results of the Kruskal-Wallis (K-W) test and Sequential Forward Floating Selection (SFFS). The results proved that the optimal input was all PSD features. Moreover, the study analyzed the effects of electroencephalography (EEG) features from distinct brain regions and PSD changes across different MWL levels to further assess the proposed system's performance. A 10-fold cross-validation was performed on six classifiers, and the optimal accuracy of 87.57% was attained using a multi-class K-Nearest Neighbor (KNN) classifier for classifying different MWL levels. The findings indicate that the wireless headset-based system is reliable and feasible. Consequently, numerous wireless EEG device-based systems can be developed for application in diverse real-driving scenarios. Additionally, the current system contributes to future research on actual flight conditions.

An adaptive unknown input approach to brain wave EEG estimation

Objective:Real time, high fidelity estimation and reconstruction of brain wave dynamics is complicated by the nonstationary and nonlinear effects of electroencephalography (EEG) measures. This work aims to introduce a novel state space architecture, which continuously updates a linear brain wave model and estimates the exogenous input to the brain wave system. This continuous update is adaptive, because the estimator continuously monitors its own performance and can modify its own parameters by a closed loop action. This highly nonlinear estimator should reconstruct the EEG measure in real time for analysis and diagnostic information. Methods:The development of this adaptive unknown input estimator focuses on treating the known phenomena of brain waves. The adaptive law accounts for nonlinearities in the EEG measure while the input estimator simultaneously accounts for the exogenous input to the system. The estimator is further robust to general process uncertainty in the plant dynamics. Results:The stability of the adaptive unknown input estimator for EEG measures is shown. This estimator is shown to outperform standard linear models, such as Kalman filtering techniques especially at points where the EEG measure changes sharply. Conclusion:This adaptive unknown input estimator reconstructs unmodeled EEG data in real time by accounting for the nonlinear brain wave dynamics. Because the adaptive law updates a linear model over time, much of the interpretability and intuition of linear state space modeling is preserved.

Clinical significance of electroencephalography power spectrum density and functional connection analysis in neonates with hypoxic-ischemic encephalopathy.

To investigate the effects of electroencephalography (EEG) power and functional connectivity analysis on cerebral cortex function in neonates with hypoxic-ischemic encephalopathy (HIE), and to identify the neurobiological indicators of neural development in HIE. We recruited 20 mild HIE neonates, 15 moderate HIE neonates, and 30 controls. EEG was performed about 72hr after birth. The power spectral density (PSD) and imaginary part of coherency (ICOH) were analyzed. Gesell developmental schedule (GDS) was used to evaluate the neural development in the mild and moderate HIE groups at 1year of age, and the correlation between the quantitative EEG results and the state of neural development was analyzed. Compared with the controls, FP1, FP2, C3, C4, Fz, Cz, Pz , F3, and P4 of moderate HIE neonates showed that the PSD of theta, alpha and beta bands decreased significantly. In terms of the mean whole-brain PSD, themoderate HIEgroupshowed a significant decrease in all frequency bands. ICOH of the moderate HIE group showed that functional connectivity was significantly less than that in the controls mainly in the delta band, and the functional connectivity of the delta, theta, alpha1, and alpha2 bands was markedly reduced compared with the mild HIE. GDS test at 1year old showed that two infants in the moderate HIE group had suspected neurological delay in gross motor and language. The developmental quotient(DQ) of gross motor, language, and personal-social ability in the moderate HIE group were significantly lower than in the mild HIE group. And there was a significant positive correlation between PSD in each EEG frequency band and GDS score in the moderate HIE group. PSD and ICOH can be used to evaluate brain function. PSD can detect the delayed neurological development in infants with moderate HIE, and can be a neurobiomarker of brain development in HIE.

Effects of electroencephalography and regional cerebral oxygen saturation monitoring on perioperative neurocognitive disorders: a systematic review and meta-analysis

BackgroundPerioperative neurocognitive disorders (PND) is a common postoperative complication including postoperative delirium (POD), postoperative cognitive decline (POCD) or delayed neurocognitive recovery. It is still controversial whether the use of intraoperative cerebral function monitoring can decrease the incidence of PND. The purpose of this study was to evaluate the effects of different cerebral function monitoring (electroencephalography (EEG) and regional cerebral oxygen saturation (rSO2) monitoring) on PND based on the data from randomized controlled trials (RCTs).MethodsThe electronic databases of Ovid MEDLINE, PubMed, EMBASE, Cochrane Library database were systematically searched using the indicated keywords from their inception to April 2020. The odds ratio (OR) or mean difference (MD) with 95% confidence interval (CI) were employed to analyze the data. Heterogeneity across analyzed studies was assessed with chi-square test and I2 test.ResultsTwenty two RCTs with 6356 patients were included in the final analysis. Data from 12 studies including 4976 patients were analyzed to assess the association between the EEG-guided anesthesia and PND. The results showed that EEG-guided anesthesia could reduce the incidence of POD in patients undergoing non-cardiac surgery (OR: 0.73; 95% CI: 0.57–0.95; P = 0.02), but had no effect on patients undergoing cardiac surgery (OR: 0.44; 95% CI: 0.05–3.54; P = 0.44). The use of intraoperative EEG monitoring reduced the incidence of POCD up to 3 months after the surgery (OR: 0.69; 95% CI: 0.49–0.96; P = 0.03), but the incidence of early POCD remained unaffected (OR: 0.61; 95% CI: 0.35–1.07; P = 0.09). The remaining 10 studies compared the effect of rSO2 monitoring to routine care in a total of 1380 participants on the incidence of PND. The results indicated that intraoperative monitoring of rSO2 could reduce the incidence of POCD (OR 0.53, 95% CI 0.39–0.73; P < 0.0001), whereas no significant difference was found regarding the incidence of POD (OR: 0.74; 95% CI: 0.48–1.14; P = 0.17).ConclusionsThe findings in the present study indicated that intraoperative use of EEG or/and rSO2 monitor could decrease the risk of PND.Trial registrationPROSPREO registration number: CRD42019130512.

Research on EEG for virtual driving between visual and auditory perception

Objective To evaluate the effect of electroencephalography(EEG) recognition rate resulted by stimulus of visual, auditory and combined perception. Methods In virtual traffic environment, the stimuli were designed in traffic information based on visual, auditory and audio-visual fusion. When one of the three stimuli appeared, the subjects completed starting and braking vehicle by imaginary using of right and left hands at the same time. The EEG signals were recorded and the imaginary motion-related features from C3, C4 were extracted and classified. Results The best recognition rates in visual, auditory and fusion perception were 100％, 100％and 83％, respectively, while the averages of the rates were 68.8％ 、82.2％、76.9％, respectively. Conclusion The recognition rates are affected by the audio, visual and fusion stimulus and apparent individual differences exist. Key words: Stimulus; Visual and auditory perception; Driving behavior; Recognition rate