Exploring the Brain Responses to Driving Fatigue through Simultaneous EEG and fNIRS Measurements

© 2020 World Scientific Publishing Company. Fatigue is one problem with driving as it can lead to difficulties with sustaining attention, behavioral lapses, and a tendency to ignore vital information or operations. In this research, we explore multimodal physiological phenomena in response to driving fatigue through simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) recordings with the aim of investigating the relationships between hemodynamic and electrical features and driving performance. Sixteen subjects participated in an event-related lane-deviation driving task while measuring their brain dynamics through fNIRS and EEGs. Three performance groups, classified as Optimal, Suboptimal, and Poor, were defined for comparison. From our analysis, we find that tonic variations occur before a deviation, and phasic variations occur afterward. The tonic results show an increased concentration of oxygenated hemoglobin (HbO2) and power changes in the EEG theta, alpha, and beta bands. Both dynamics are significantly correlated with deteriorated driving performance. The phasic EEG results demonstrate event-related desynchronization associated with the onset of steering vehicle in all power bands. The concentration of phasic HbO2 decreased as performance worsened. Further, the negative correlations between tonic EEG delta and alpha power and HbO2 oscillations suggest that activations in HbO2 are related to mental fatigue. In summary, combined hemodynamic and electrodynamic activities can provide complete knowledge of the brain's responses as evidence of state changes during fatigue driving

Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)

© 1993-2012 IEEE. Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77\%, and at the same time increase the correlation to the true response speed by 19.39-86.47\%

The effects of different fatigue levels on brain–behavior relationships in driving

© 2019 The Authors. Brain and Behavior published by Wiley Periodicals, Inc. Background: In the past decade, fatigue has been regarded as one of the main factors impairing task performance and increasing behavioral lapses during driving, even leading to fatal car crashes. Although previous studies have explored the impact of acute fatigue through electroencephalography (EEG) signals, it is still unclear how different fatigue levels affect brain–behavior relationships. Methods: A longitudinal study was performed to investigate the brain dynamics and behavioral changes in individuals under different fatigue levels by a sustained attention task. This study used questionnaires in combination with actigraphy, a noninvasive means of monitoring human physiological activity cycles, to conduct longitudinal assessment and tracking of the objective and subjective fatigue levels of recruited participants. In this study, degrees of effectiveness score (fatigue rating) are divided into three levels (normal, reduced, and high risk) by the SAFTE fatigue model. Results: Results showed that those objective and subjective indicators were negatively correlated to behavioral performance. In addition, increased response times were accompanied by increased alpha and theta power in most brain regions, especially the posterior regions. In particular, the theta and alpha power dramatically increased in the high-fatigue (high-risk) group. Additionally, the alpha power of the occipital regions showed an inverted U-shaped change. Conclusion: Our results help to explain the inconsistent findings among existing studies, which considered the effects of only acute fatigue on driving performance while ignoring different levels of resident fatigue, and potentially lead to practical and precise biomathematical models to better predict the performance of human operators

Positive effects of methylphenidate on hyperactivity are moderated by monoaminergic gene variants in children with autism spectrum disorders.

Methylphenidate (MPH) reduces hyperactive-impulsive symptoms common in children with autism spectrum disorders (ASDs), however, response and tolerability varies widely. We hypothesized monoaminergic gene variants may moderate MPH effects in ASD, as in typically developing children with attention-deficit/hyperactivity disorder. Genotype data were available for 64 children with ASD and hyperactivity who were exposed to MPH during a 1-week safety/tolerability lead-in phase and 58 who went on to be randomized to placebo and three doses of MPH during a 4-week blinded, crossover study. Outcome measures included the Clinical Global Impression-Improvement (CGI-I) scale and the Aberrant Behavior Checklist (ABC-hyperactivity index). A total of 14 subjects discontinued the study because of MPH side effects. Subjects were genotyped for variants in DRD1-DRD5, ADRA2A, SLC6A3, SLC6A4, MAOA and MAOB, and COMT. Forty-nine percent of the sample met positive responder criteria. In this modest but relatively homogeneous sample, significant differences by DRD1 (P=0.006), ADRA2A (P<0.02), COMT (P<0.04), DRD3 (P<0.05), DRD4 (P<0.05), SLC6A3 (P<0.05) and SLC6A4 (P<0.05) genotypes were found for responders versus non-responders. Variants in DRD2 (P<0.001) and DRD3 (P<0.04) were associated with tolerability in the 14 subjects who discontinued the trial. For this first MPH pharmacogenetic study in children with ASD, multiple monoaminergic gene variants may help explain individual differences in MPH's efficacy and tolerability

An inflatable and wearable wireless system for making 32-channel electroencephalogram measurements

© 2001-2011 IEEE. Potable electroencephalography (EEG) devices have become critical for important research. They have various applications, such as in brain-computer interfaces (BCI). Numerous recent investigations have focused on the development of dry sensors, but few concern the simultaneous attachment of high-density dry sensors to different regions of the scalp to receive qualified EEG signals from hairy sites. An inflatable and wearable wireless 32-channel EEG device was designed, prototyped, and experimentally validated for making EEG signal measurements; it incorporates spring-loaded dry sensors and a novel gasbag design to solve the problem of interference by hair. The cap is ventilated and incorporates a circuit board and battery with a high-tolerance wireless (Bluetooth) protocol and low power consumption characteristics. The proposed system provides a 500/250 Hz sampling rate, and 24 bit EEG data to meet the BCI system data requirement. Experimental results prove that the proposed EEG system is effective in measuring audio event-related potential, measuring visual event-related potential, and rapid serial visual presentation. Results of this work demonstrate that the proposed EEG cap system performs well in making EEG measurements and is feasible for practical applications

Forehead EEG in Support of Future Feasible Personal Healthcare Solutions: Sleep Management, Headache Prevention, and Depression Treatment

© 2013 IEEE. There are current limitations in the recording technologies for measuring EEG activity in clinical and experimental applications. Acquisition systems involving wet electrodes are time-consuming and uncomfortable for the user. Furthermore, dehydration of the gel affects the quality of the acquired data and reliability of long-term monitoring. As a result, dry electrodes may be used to facilitate the transition from neuroscience research or clinical practice to real-life applications. EEG signals can be easily obtained using dry electrodes on the forehead, which provides extensive information concerning various cognitive dysfunctions and disorders. This paper presents the usefulness of the forehead EEG with advanced sensing technology and signal processing algorithms to support people with healthcare needs, such as monitoring sleep, predicting headaches, and treating depression. The proposed system for evaluating sleep quality is capable of identifying five sleep stages to track nightly sleep patterns. Additionally, people with episodic migraines can be notified of an imminent migraine headache hours in advance through monitoring forehead EEG dynamics. The depression treatment screening system can predict the efficacy of rapid antidepressant agents. It is evident that frontal EEG activity is critically involved in sleep management, headache prevention, and depression treatment. The use of dry electrodes on the forehead allows for easy and rapid monitoring on an everyday basis. The advances in EEG recording and analysis ensure a promising future in support of personal healthcare solutions

Integrated photonic quantum gates for polarization qubits

Integrated photonic circuits have a strong potential to perform quantum information processing. Indeed, the ability to manipulate quantum states of light by integrated devices may open new perspectives both for fundamental tests of quantum mechanics and for novel technological applications. However, the technology for handling polarization encoded qubits, the most commonly adopted approach, is still missing in quantum optical circuits. Here we demonstrate the first integrated photonic Controlled-NOT (CNOT) gate for polarization encoded qubits. This result has been enabled by the integration, based on femtosecond laser waveguide writing, of partially polarizing beam splitters on a glass chip. We characterize the logical truth table of the quantum gate demonstrating its high fidelity to the expected one. In addition, we show the ability of this gate to transform separable states into entangled ones and vice versa. Finally, the full accessibility of our device is exploited to carry out a complete characterization of the CNOT gate through a quantum process tomography.Comment: 6 pages, 4 figure

Consistent model of magnetism in ferropnictides

The discovery of superconductivity in LaFeAsO introduced the ferropnictides as a major new class of superconducting compounds with critical temperatures second only to cuprates. The presence of magnetic iron makes ferropnictides radically different from cuprates. Antiferromagnetism of the parent compounds strongly suggests that superconductivity and magnetism are closely related. However, the character of magnetic interactions and spin fluctuations in ferropnictides, in spite of vigorous efforts, has until now resisted understanding within any conventional model of magnetism. Here we show that the most puzzling features can be naturally reconciled within a rather simple effective spin model with biquadratic interactions, which is consistent with electronic structure calculations. By going beyond the Heisenberg model, this description explains numerous experimentally observed properties, including the peculiarities of the spin wave spectrum, thin domain walls, crossover from first to second order phase transition under doping in some compounds, and offers new insight in the occurrence of the nematic phase above the antiferromagnetic phase transition.Comment: 5 pages, 3 figures, revtex

Digital Quantum Simulation of the Statistical Mechanics of a Frustrated Magnet

Many interesting problems in physics, chemistry, and computer science are equivalent to problems of interacting spins. However, most of these problems require computational resources that are out of reach by classical computers. A promising solution to overcome this challenge is to exploit the laws of quantum mechanics to perform simulation. Several "analog" quantum simulations of interacting spin systems have been realized experimentally. However, relying on adiabatic techniques, these simulations are limited to preparing ground states only. Here we report the first experimental results on a "digital" quantum simulation on thermal states; we simulated a three-spin frustrated magnet, a building block of spin ice, with an NMR quantum information processor, and we are able to explore the phase diagram of the system at any simulated temperature and external field. These results serve as a guide for identifying the challenges for performing quantum simulation on physical systems at finite temperatures, and pave the way towards large scale experimental simulations of open quantum systems in condensed matter physics and chemistry.Comment: 7 pages for the main text plus 6 pages for the supplementary material