Kinesthesia in a sustained-attention driving task

This study investigated the effects of kinesthetic stimuli on brain activities during a sustained-attention task in an immersive driving simulator. Tonic and phasic brain responses on multiple timescales were analyzed using time-frequency analysis of electroencephalographic (EEG) sources identified by independent component analysis (ICA). Sorting EEG spectra with respect to reaction times (RT) to randomly introduced lane-departure events revealed distinct effects of kinesthetic stimuli on the brain under different performance levels. Experimental results indicated that EEG spectral dynamics highly correlated with performance lapses when driving involved kinesthetic feedback. Furthermore, in the realistic environment involving both visual and kinesthetic feedback, a transitive relationship of power spectra between optimal-, suboptimal-, and poor-performance groups was found predominately across most of the independent components. In contrast to the static environment with visual input only, kinesthetic feedback reduced theta-power augmentation in the central and frontal components when preparing for action and error monitoring, while strengthening alpha suppression in the central component while steering the wheel. In terms of behavior, subjects tended to have a short response time to process unexpected events with the assistance of kinesthesia, yet only when their performance was optimal. Decrease in attentional demand, facilitated by kinesthetic feedback, eventually significantly increased the reaction time in the suboptimal-performance state. Neurophysiological evidence of mutual relationships between behavioral performance and neurocognition in complex task paradigms and experimental environments, presented in this study, might elucidate our understanding of distributed brain dynamics, supporting natural human cognition and complex coordinated, multi-joint naturalistic behavior, and lead to improved understanding of brain-behavior relations in operating environments. © 2014 Elsevier Inc

An EEG-Based Fatigue Detection and Mitigation System

© 2016 World Scientific Publishing Company. Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual's internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant's EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha-and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject's cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments

Theta and alpha oscillations in attentional interaction during distracted driving

© 2018 Wang, Jung and Lin. Performing multiple tasks simultaneously usually affects the behavioral performance as compared with executing the single task. Moreover, processing multiple tasks simultaneously often involve more cognitive demands. Two visual tasks, lane-keeping task and mental calculation, were utilized to assess the brain dynamics through 32-channel electroencephalogram (EEG) recorded from 14 participants. A 400-ms stimulus onset asynchrony (SOA) factor was used to induce distinct levels of attentional requirements. In the dual-task conditions, the deteriorated behavior reflected the divided attention and the overlapping brain resources used. The frontal, parietal and occipital components were decomposed by independent component analysis (ICA) algorithm. The event- and response-related theta and alpha oscillations in selected brain regions were investigated first. The increased theta oscillation in frontal component and decreased alpha oscillations in parietal and occipital components reflect the cognitive demands and attentional requirements as executing the designed tasks. Furthermore, time-varying interactive over-additive (O-Add), additive (Add) and under-additive (U-Add) activations were explored and summarized through the comparison between the summation of the elicited spectral perturbations in two single-task conditions and the spectral perturbations in the dual task. Add and U-Add activations were observed while executing the dual tasks. U-Add theta and alpha activations dominated the posterior region in dual-task situations. Our results show that both deteriorated behaviors and interactive brain activations should be comprehensively considered for evaluating workload or attentional interaction precisely

High-density information storage in an absolutely defined aperiodic sequence of monodisperse copolyester

Synthesis of a polymer composed of a large discrete number of chemically distinct monomers in an absolutely defined aperiodic sequence remains a challenge in polymer chemistry. The synthesis has largely been limited to oligomers having a limited number of repeating units due to the difficulties associated with the step-by-step addition of individual monomers to achieve high molecular weights. Here we report the copolymers of ??-hydroxy acids, poly(phenyllactic-co-lactic acid) (PcL) built via the cross-convergent method from four dyads of monomers as constituent units. Our proposed method allows scalable synthesis of sequence-defined PcL in a minimal number of coupling steps from reagents in stoichiometric amounts. Digital information can be stored in an aperiodic sequence of PcL, which can be fully retrieved as binary code by mass spectrometry sequencing. The information storage density (bit/Da) of PcL is 50% higher than DNA, and the storage capacity of PcL can also be increased by adjusting the molecular weight (~38???kDa)

Charged-Higgs phenomenology in the Aligned two-Higgs-doublet model

The alignment in flavour space of the Yukawa matrices of a general two-Higgs-doublet model results in the absence of tree-level flavour-changing neutral currents. In addition to the usual fermion masses and mixings, the aligned Yukawa structure only contains three complex parameters, which are potential new sources of CP violation. For particular values of these three parameters all known specific implementations of the model based on discrete Z_2 symmetries are recovered. One of the most distinctive features of the two-Higgs-doublet model is the presence of a charged scalar. In this work, we discuss its main phenomenological consequences in flavour-changing processes at low energies and derive the corresponding constraints on the parameters of the aligned two-Higgs-doublet model.Comment: 46 pages, 19 figures. Version accepted for publication in JHEP. References added. Discussion slightly extended. Conclusions unchange

Toward a new cognitive neuroscience: Modeling natural brain dynamics

Decades of brain imaging experiments have revealed important insights into the architecture of the human brain and the detailed anatomic basis for the neural dynamics supporting human cognition. However, technical restrictions of traditional brain imaging approaches including functional magnetic resonance tomography (fMRI), positron emission tomography (PET), and magnetoencephalography (MEG) severely limit participants' movements during experiments. As a consequence, our knowledge of the neural basis of human cognition is rooted in a dissociation of human cognition from what is arguably its foremost, and certainly its evolutionarily most determinant function, organizing our behavior so as to optimize its consequences in our complex, multi-scale, and ever-changing environment. The concept of natural cognition, therefore, should not be separated from our fundamental experience and role as embodied agents acting in a complex, partly unpredictable world. To gain new insights into the brain dynamics supporting natural cognition, we must overcome restrictions of traditional brain imaging technology. First, the sensors used must be lightweight and mobile to allow monitoring of brain activity during free participant movements. New hardware technology for electroencephalography (EEG) and near infrared spectroscopy (NIRS) allows recording electrical and hemodynamic brain activity while participants are freely moving. New data-driven analysis approaches must allow separation of signals arriving at the sensors from the brain and from non-brain sources (neck muscles, eyes, heart, the electrical environment, etc.). Independent component analysis (ICA) and related blind source separation methods allow separation of brain activity from non-brain activity from data recorded during experimental paradigms that stimulate natural cognition. Imaging the precisely timed, distributed brain dynamics that support all forms of our motivated actions and interactions in both laboratory and real-world settings requires new modes of data capture and of data processing. Synchronously recording participants’ motor behavior, brain activity, and other physiology, as well as their physical environment and external events may be termed mobile brain/body imaging ('MoBI'). Joint multi-stream analysis of recorded MoBI data is a major conceptual, mathematical, and data processing challenge. This Research Topic is one result of the first international MoBI meeting in Delmenhorst Germany in September 2013. During an intense workshop researchers from all over the world presented their projects and discussed new technological developments and challenges of this new imaging approach. Several of the presentations are compiled in this Research Topic that we hope may inspire new research using the MoBI paradigm to investigate natural cognition by recording and analyzing the brain dynamics and behavior of participants performing a wide range of naturally motivated actions and interactions

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\%

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

ACL injuries identifiable for pre-participation imagiological analysis: Risk factors

Identification of pre-participation risk factors for noncontact anterior cruciate ligament (ACL) injuries has been attracting a great deal of interest in the sports medicine and traumatology communities. Appropriate methods that enable predicting which patients could benefit from pre- ventive strategies are most welcome. This would enable athlete-specific training and conditioning or tailored equipment in order to develop appropriate strategies to reduce incidence of injury. In order to accomplish these goals, the ideal system should be able to assess both anatomic and functional features. Complementarily, the screening method must be cost-effective and suited for widespread application. Anatomic study protocol requiring only standard X rays could answer some of such demands. Dynamic MRI/CT evaluation and electronically assisted pivot-shift evaluation can be powerful tools providing complementary information. These upcoming insights, when validated and properly combined, envision changing pre-participation knee examination in the near future. Herein different methods (validated or under research) aiming to improve the capacity to identify persons/athletes with higher risk for ACL injury are overviewed.