@article{rice2019gender,

title = {Gender Differences in Dry-EEG Manifestations During Acute and Insidious Normobaric Hypoxia},

author = {Merrill G Rice and Dallas Snider and Sabrina Drollinger and Chris Greil and Frank Bogni and Jeffrey Phillips and Anil Raj and Katherine Marco and Steven Linnville},

doi = { https://doi.org/10.3357/AMHP.5227.2019},

year  = {2019},

date = {2019-04-01},

journal = {Aerospace Medicine and Human Performance},

volume = {90},

number = {4},

pages = {369--377},

publisher = {Aerospace Medical Association},

abstract = {INTRODUCTION: Prior research suggests there may be gender differences with regards to hypoxia resilience. Our study was designed to determine whether there were differences between genders in neuronal electrical activity at simulated altitude and whether those changes correlated with cognitive and aviation performance decrements.



METHODS: There were 60 student Naval Aviators or Flight Officers who completed this study (30 women, 30 men). Participants were exposed to increasing levels of normobaric hypoxia and monitored with dry EEG while flying a fixed-base flight simulation. Gender differences in brainwave frequency power were quantified using MATLAB. Changes in flight and cognitive performance were analyzed via simulation tasks and with a cognitive test validated under hypoxia.



RESULTS: Significant decreases in theta and gamma frequency power occurred for women compared to men with insidious hypoxic exposures to 20K, with an average frequency power decrease for women of 19.4% compared to 9.3% for men in theta, and a 42.2% decrease in gamma for women compared to 21.7% for men. Beta frequency power correlated highest between genders, with an average correlation coefficient of r = 0.95 across seven channels.



DISCUSSION: Results of this study suggest there is identifiable brain wave suppression for both men and women with hypoxic exposure and, moreover, there are significant differences in this suppression between genders. Beta frequency power was most sensitive for both genders and highly correlative compared to other brainwave frequencies. The implications of these findings are important considerations for next-generation aviation helmets, which may employ this technology as an early warning mechanism.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{di2019dry,

title = {The Dry Revolution: Evaluation of Three Different EEG Dry Electrode Types in Terms of Signal Spectral Features, Mental States Classification and Usability},

author = {Gianluca Di Flumeri and Pietro Aric{`o} and Gianluca Borghini and Nicolina Sciaraffa and Antonello Di Florio and Fabio Babiloni},

doi = {https://doi.org/10.3390/s19061365},

year  = {2019},

date = {2019-03-19},

journal = {Sensors},

volume = {19},

number = {6},

pages = {1365},

publisher = {Multidisciplinary Digital Publishing Institute},

abstract = {One century after the first recording of human electroencephalographic (EEG) signals, EEG has become one of the most used neuroimaging techniques. The medical devices industry is now able to produce small and reliable EEG systems, enabling a wide variety of applications also with no-clinical aims, providing a powerful tool to neuroscientific research. However, these systems still suffer from a critical limitation, consisting in the use of wet electrodes, that are uncomfortable and require expertise to install and time from the user. In this context, dozens of different concepts of EEG dry electrodes have been recently developed, and there is the common opinion that they are reaching traditional wet electrodes quality standards. However, although many papers have tried to validate them in terms of signal quality and usability, a comprehensive comparison of different dry electrode types from multiple points of view is still missing. The present work proposes a comparison of three different dry electrode types, selected among the main solutions at present, against wet electrodes, taking into account several aspects, both in terms of signal quality and usability. In particular, the three types consisted in gold-coated single pin, multiple pins and solid-gel electrodes. The results confirmed the great standards achieved by dry electrode industry, since it was possible to obtain results comparable to wet electrodes in terms of signals spectra and mental states classification, but at the same time drastically reducing the time of montage and enhancing the comfort. In particular, multiple-pins and solid-gel electrodes overcome gold-coated single-pin-based ones in terms of comfort.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rice2019dry,

title = {Dry-EEG Manifestations of Acute and Insidious Hypoxia During Simulated Flight},

author = {Merrill G Rice and Dallas Snider and Sabrina Drollinger and Chris Greil and Frank Bogni and Jeffrey Phillips and Anil Raj and Katherine Marco and Steven Linnville},

doi = {https://doi.org/10.3357/AMHP.5228.2019},

year  = {2019},

date = {2019-02-01},

journal = {Aerospace Medicine and Human Performance},

volume = {90},

number = {2},

pages = {92-100},

publisher = {Aerospace Medical Association},

abstract = {INTRODUCTION: Recently, portable dry electroencephalographs (dry-EEGs) have indexed cognitive workload, fatigue, and drowsiness in operational environments. Using this technology this project assessed whether significant changes in brainwave frequency power occurred in response to hypoxic exposures as experienced in military aviation.



METHODS: There were 60 (30 women, 30 men) student Naval Aviators or Flight Officers who were exposed to an intense (acute) high-altitude (25,000 ft) normobaric hypoxic exposure, and 20 min later, more gradual (insidious) normobaric hypoxic exposure up to 20,000 ft while flying a fixed-wing flight simulation and monitored with a dry-EEG system. Using MATLAB, EEG frequencies and power were quantified and analyzed. Cognitive performance was also assessed with a cognitive task validated under hypoxia. Normobaric hypoxia and O2 saturation (Spo 2) were produced and monitored using the Reduced Oxygen Breathing Device (ROBD2).



RESULTS: Significant Spo 2 decreases were recorded at acute 25K and insidious 20K simulated altitudes. Significant power decreases were recorded in all frequencies (alpha, beta, gamma, and theta) and all channels with acute 25K exposures. Gamma, beta, and theta frequency power were significantly decreased with insidious 20K exposures at most of the channels. The frequency power decreases corresponded to significant decreases in cognitive performance and flight performance. Most importantly, frequency power suppressions occurred despite 42% of the volunteers not perceiving they were hypoxic in the acute phase, nor 20% in the insidious phase.



DISCUSSION: Results suggest EEG suppression during acute/insidious hypoxia can index performance decrements. These findings have promising implications in the development of biosensors that mitigate potential in-flight hypoxic physiological episodes.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lim2019multiple,

title = {Multiple-command single-frequency SSVEP-based BCI system using flickering action video},

author = {Hyunmi Lim and Jeonghun Ku},

doi = {https://doi.org/10.1016/j.jneumeth.2019.01.005},

year  = {2019},

date = {2019-01-16},

journal = {Journal of Neuroscience Methods},

volume = {314},

pages = {21-27},

publisher = {Elsevier},

abstract = {Background

The number of commands in a brain–computer interface (BCI) system is important. This study proposes a new BCI technique to increase the number of commands in a single BCI system without loss of accuracy.



New method

We expected that a flickering action video with left and right elbow movements could simultaneously activate the different pattern of event-related desynchronization (ERD) according to the video contents (e.g., left or right) and steady-state visually evoked potential (SSVEP). The classification accuracy to discriminate left, right, and rest states was compared under the three following feature combinations: SSVEP power (19–21 Hz), Mu power (8–13 Hz), and simultaneous SSVEP and Mu power.



Results

The SSVEP feature could discriminate the stimulus condition, regardless of left or right, from the rest condition, while the Mu feature discriminated left or right, but was relatively poor in discriminating stimulus from rest. However, combining the SSVEP and Mu features, which were evoked by the stimulus with a single frequency, showed superior performance for discriminating all the stimuli among rest, left, or right.



Comparison with the existing method

The video contents could activate the ERD differently, and the flickering component increased its accuracy, such that it revealed a better performance to discriminate when considering together.



Conclusions

This paradigm showed possibility of increasing performance in terms of accuracy and number of commands with a single frequency by applying flickering action video paradigm and applicability to rehabilitation systems used by patients to facilitate their mirror neuron systems while training.},

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{pereira2018cross,

title = {Cross-Subject EEG Event-Related Potential Classification for Brain-Computer Interfaces Using Residual Networks},

author = {Arnaldo Pereira and Dereck Padden and Jay Jantz and Kate Lin and Ramses Alcaide-Aguirre},

doi = {10.13140/RG.2.2.16257.10086},

year  = {2018},

date = {2018-09-20},

urldate = {2018-01-01},

abstract = {EEG event-related potentials, and the P300 signal in

particular, are promising modalities for brain-computer interfaces (BCI). But the nonstationarity of EEG signals and

their differences across individuals have made it difficult to

implement classifiers that can determine user intent without having to be retrained or calibrated for each new user

and sometimes even each session. This is a major impediment to the development of consumer BCI. Recently, the

EEG BCI literature has begun to apply convolutional neural

networks (CNNs) for classification, but experiments have

largely been limited to training and testing on single subjects. In this paper, we report a study in which EEG data

were recorded from 66 subjects in a visual oddball task

in virtual reality. Using wide residual networks (WideResNets), we obtain state-of-the-art performance on a test set

composed of data from all 66 subjects together. Additionally, a minimal preprocessing stream to convert EEG data

into square images for CNN input while adding regularization is presented and shown to be viable. This study also

provides some guidance on network architecture parameters based on experiments with different models. Our results show that it may possible with enough data to train

a classifier for EEG-based BCIs that can generalize across

individuals without the need for individual training or calibration.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}