@article{hu2022assessing,

title = {Assessing stroke rehabilitation degree based on quantitative EEG index and nonlinear parameters},

author = {Yuxia Hu and Yufei Wang and Rui Zhang and Yubo Hu and Mingzhu Fang and Zhe Li and Li Shi and Yankun Zhang and Zhong Zhang and Jinfeng Gao and others},

url = {https://link.springer.com/article/10.1007/s11571-022-09849-4},

year  = {2022},

date = {2022-08-06},

urldate = {2022-01-01},

journal = {Cognitive Neurodynamics},

pages = {1--9},

publisher = {Springer},

abstract = {The assessment of motor function is critical to the rehabilitation of stroke patients. However, commonly used evaluation methods are based on behavior scoring, which lacks neurological indicators that directly reflect the motor function of the brain. The objective of this study was to investigate whether resting-state EEG indicators could improve stroke rehabilitation evaluation. We recruited 68 participants and recorded their resting-state EEG data. According to Brunnstrom stage, the participants were divided into three groups: severe, moderate, and mild. Ten quantitative electroencephalographic (QEEG) and five non-linear parameters of resting-state EEG were calculated for further analysis. Statistical tests were performed, and the genetic algorithm-support vector machine was used to select the best feature combination for classification. We found the QEEG parameters show significant differences in Delta, Alpha1, Alpha2, DAR, and DTABR (P < 0.05) among the three groups. Regarding nonlinear parameters, ApEn, SampEn, Lz, and C0 showed significant differences (P < 0.05). The optimal feature classification combination accuracy rate reached 85.3%. Our research shows that resting-state EEG indicators could be used for stroke rehabilitation evaluation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{arakaki2019alpha,

title = {Alpha desynchronization during simple working memory unmasks pathological aging in cognitively healthy individuals},

author = {Xianghong Arakaki and Ryan Lee and Kevin S King and Alfred N Fonteh and Michael G Harrington},

editor = {Stephen D. Ginsberg, Nathan S Kline Institute},

doi = {https://doi.org/10.1371/journal.pone.0208517},

year  = {2019},

date = {2019-01-02},

journal = {PloS one},

volume = {14},

number = {1},

pages = {e0208517},

publisher = {Public Library of Science San Francisco, CA USA},

abstract = {Our aim is to explore if cognitive challenge combined with objective physiology can reveal abnormal frontal alpha event-related desynchronization (ERD), in early Alzheimer’s disease (AD). We used quantitative electroencephalography (qEEG) to investigate brain activities during N-back working memory (WM) processing at two different load conditions (N = 0 or 2) in an aging cohort. We studied 60–100 year old participants, with normal cognition, and who fits one of two subgroups from cerebrospinal fluid (CSF) proteins: cognitively healthy (CH) with normal amyloid/tau ratio (CH-NAT, n = 10) or pathological amyloid/tau ratio (CH-PAT, n = 14). We recorded behavioral performances, and analyzed alpha power and alpha spectral entropy (SE) at three occasions: during the resting state, and at event-related desynchronization (ERD) [250 ~ 750 ms] during 0-back and 2-back. During 0-back WM testing, the behavioral performance was similar between the two groups, however, qEEG notably differentiated CH-PATs from CH-NATs on the simple, 0-back testing: Alpha ERD decreased from baseline only in the parietal region in CH-NATs, while it decreased in all brain regions in CH-PATs. Alpha SE did not change in CH-NATs, but was increased from baseline in the CH-PATs in frontal and left lateral regions (p<0.01), and was higher in the frontal region (p<0.01) of CH-PATs compared to CH-NATs. The alpha ERD and SE analyses suggest there is frontal lobe dysfunction during WM processing in the CH-PAT stage. Additional power and correlations with behavioral performance were also explored. This study provide pilot information to further evaluate whether this biomarker has clinical significance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hunter2018change,

title = {Change in quantitative EEG theta cordance as a potential predictor of repetitive transcranial magnetic stimulation clinical outcome in major depressive disorder},

author = {Aimee M Hunter and Thien X Nghiem and Ian A Cook and David E Krantz and Michael J Minzenberg and Andrew F Leuchter},

doi = {https://doi.org/10.1177/1550059417746212},

year  = {2017},

date = {2017-12-10},

urldate = {2017-12-10},

journal = {Clinical EEG and Neuroscience},

volume = {49},

number = {5},

pages = {306--315},

publisher = {Sage Publications Sage CA: Los Angeles, CA},

abstract = {Repetitive transcranial magnetic stimulation (rTMS) has demonstrated efficacy in major depressive disorder (MDD), although clinical outcome is variable. Change in the resting-state quantitative electroencephalogram (qEEG), particularly in theta cordance early in the course of treatment, has been linked to antidepressant medication outcomes but has not been examined extensively in clinical rTMS. This study examined change in theta cordance over the first week of clinical rTMS and sought to identify a biomarker that would predict outcome at the end of 6 weeks of treatment. Clinically stable outpatients (n = 18) received nonblinded rTMS treatment administered to the dorsolateral prefrontal cortex (DLPFC). Treatment parameters (site, intensity, number of pulses) were adjusted on an ongoing basis guided by changes in symptom severity rating scale scores. qEEGs were recorded at pretreatment baseline and after 1 week of left DLPFC (L-DLPFC) rTMS using a 21-channel dry-electrode headset. Analyses examined the association between week 1 regional changes in theta band (4-8 Hz) cordance, and week 6 patient- and physician-rated outcomes. Theta cordance change in the central brain region predicted percent change in Inventory of Depressive Symptomology–Self-Report (IDS-SR) score, and improvement versus nonimprovement on the Clinical Global Impression–Improvement Inventory (CGI-I) (R2 = .38, P = .007; and Nagelkerke R2 = .78, P = .0001, respectively). The cordance biomarker remained significant when controlling for age, gender, and baseline severity. Treatment-emergent change in EEG theta cordance in the first week of rTMS may predict acute (6-week) treatment outcome in MDD. This oscillatory synchrony biomarker merits further study in independent samples.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fridman2016evaluation,

title = {Evaluation of dry sensors for neonatal EEG recordings},

author = {Igor Fridman and Malaika Cordeiro and Khodayar Rais-Bahrami and Neil J McDonald and James J Reese Jr and An N Massaro and Joan A Conry and Taeun Chang and Walid Soussou and Tammy N Tsuchida},

url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4818163/},

year  = {2016},

date = {2016-04-01},

journal = {Journal of clinical neurophysiology: official publication of the American Electroencephalographic Society},

volume = {33},

number = {2},

pages = {149},

publisher = {NIH Public Access},

abstract = {Introduction

Neonatal seizures are a common neurologic diagnosis in Neonatal Intensive Care Units (NICUs), occurring in approximately 14,000 newborns annually in the US. While the only reliable means of detecting and treating neonatal seizures is with an EEG recording, many neonates do not get an EEG or experience delays in getting them. Barriers to obtaining neonatal EEGs include: 1) lack of skilled EEG technologists to apply conventional wet electrodes to delicate neonatal skin, 2) poor signal quality due to improper skin preparation and artifact, 3) extensive time needed to apply electrodes. Dry sensors have the potential to overcome these obstacles but have not been previously evaluated on neonates.



Methods

Sequential and simultaneous recordings with wet and dry sensors were performed for one hour on 27 neonates from 35-42.5 weeks postmenstrual age. Recordings were analyzed for correlation and amplitude, and were reviewed by neurophysiologists. Performance of dry sensors on simulated vernix was examined.



Results

Analysis of dry and wet signals showed good time-domain correlation (reaching >0.8) given the non-superimposed sensor positions, and similar power spectral density curves. Neurophysiologist reviews showed no statistically significant difference between dry and wet data on most clinically-relevant EEG background and seizure patterns. There was no skin injury after 1 hr of dry sensor recordings. In contrast to wet electrodes, impedance and electrical artifact of dry sensors were largely unaffected by simulated vernix.



Conclusions

Dry sensors evaluated in this study have the potential to provide high-quality, timely EEG recordings on neonates with less risk of skin injury.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}