Assessing Drivers’ Vigilance State During Monotonous Driving

The differential effects of three hours of monotonous daytime driving on subjective (sleepiness, inattention, monotony), performance (choice reaction time), and physiological (EEG alpha power, P300-amplitude, heart rate) vigilance measures were examined. A linear degradation of drivers’ subjective state, mean long reaction times (as opposed to short ones), P300-amplitude and parietal alpha power with time spent on the highway was identified. An improvement of the subjective measures towards the end of the driving task was not accompanied by any improvement in performance or physiological measures. This dissociation of self-assessment and objective vigilance measures has important implications for the design of modern driver assistant systems that aim to adapt to the driver’s state

Generative discriminative models for multivariate inference and statistical mapping in medical imaging

This paper presents a general framework for obtaining interpretable multivariate discriminative models that allow efficient statistical inference for neuroimage analysis. The framework, termed generative discriminative machine (GDM), augments discriminative models with a generative regularization term. We demonstrate that the proposed formulation can be optimized in closed form and in dual space, allowing efficient computation for high dimensional neuroimaging datasets. Furthermore, we provide an analytic estimation of the null distribution of the model parameters, which enables efficient statistical inference and p-value computation without the need for permutation testing. We compared the proposed method with both purely generative and discriminative learning methods in two large structural magnetic resonance imaging (sMRI) datasets of Alzheimer's disease (AD) (n=415) and Schizophrenia (n=853). Using the AD dataset, we demonstrated the ability of GDM to robustly handle confounding variations. Using Schizophrenia dataset, we demonstrated the ability of GDM to handle multi-site studies. Taken together, the results underline the potential of the proposed approach for neuroimaging analyses.Comment: To appear in MICCAI 2018 proceeding

Relationship between regional white matter hyperintensities and alpha oscillations in older adults

Aging is associated with increased white matter hyperintensities (WMHs) and with alterations of alpha oscillations (7–13 Hz). However, a crucial question remains, whether changes in alpha oscillations relate to aging per se or whether this relationship is mediated by age-related neuropathology like WMHs. Using a large cohort of cognitively healthy older adults (N=907, 60-80 years), we assessed relative alpha power, alpha peak frequency, and long-range temporal correlations (LRTC) from resting-state EEG. We further associated these parameters with voxel-wise WMHs from 3T MRI. We found that a higher prevalence of WMHs in the superior and posterior corona radiata as well as in the thalamic radiation was related to elevated alpha power, with the strongest association in the bilateral occipital cortex. In contrast, we observed no significant relation of the WMHs probability with alpha peak frequency and LRTC. Finally, higher age was associated with elevated alpha power via total WMH volume. We suggest that an elevated alpha power is a consequence of WMH affecting a spatial organization of alpha sources

The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

Brain–computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies

Influence of Pacing Mode and Rate on Peripheral Levels of Atrial Natriuretic Peptide (ANP)

The effect of acute modifications of pacing mode and rate on plasma ANP levels was evaluated. ANP was determined in ten resting patients with ODD pacemokers due to binodal disease or intermittent second- and third-degree AV block. At 82/minute pacing rate the ANP plasma levels (normal range 2 to 30 fmol/mL) corresponded to those under AAI (4.05 ± 2.10 fmol/mL) and DDD (4.18 ± 2.02 fmol/mL) pacing, but increased significantly (P < 0.05) during VVI pacing (6.96 ± 3.70 fmol/mL). Acceleration of DDD stimulation frequency from 82 to 113/minutes led to significant increases of ANP levels by the factor of three in all chosen AV delays. The lowest ANP plasma levels were measured of 175 msec AV delay under 82/minute pacing rate in DDD mode. Under 113/minutes the differences of ANP concentration after variations of AV delays were less pronounced. The influences of altered atrial pressure and tension on ANP release are discussed to account for changes in ANP plasma levels following different modes and rates of pacemaker stimulation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75366/1/j.1540-8159.1989.tb01862.x.pd

Assessing Drivers’ Vigilance State During Monotonous Driving

The differential effects of three hours of monotonous daytime driving on subjective (sleepiness, inattention, monotony), performance (choice reaction time), and physiological (EEG alpha power, P300-amplitude, heart rate) vigilance measures were examined. A linear degradation of drivers’ subjective state, mean long reaction times (as opposed to short ones), P300-amplitude and parietal alpha power with time spent on the highway was identified. An improvement of the subjective measures towards the end of the driving task was not accompanied by any improvement in performance or physiological measures. This dissociation of self-assessment and objective vigilance measures has important implications for the design of modern driver assistant systems that aim to adapt to the driver’s state

Alterations in rhythmic and non-rhythmic resting-state EEG activity and their link to cognition in older age

While many structural and biochemical changes in the brain have been previously associated with aging, the findings concerning electrophysiological signatures, reflecting functional properties of neuronal networks, remain rather controversial. To try resolve this issue, we took advantage of a large population study (N=1703) and comprehensively investigated the association of multiple EEG biomarkers (power of alpha and theta oscillations, individual alpha peak frequency (IAF), the slope of 1/f power spectral decay), aging, and aging and cognitive performance. Cognitive performance was captured with three factors representing processing speed, episodic memory, and interference resolution. Our results show that not only did IAF decline with age but it was also associated with interference resolution over multiple cortical areas. To a weaker extent, 1/f slope of the PSD showed age-related reductions, mostly in frontal brain regions. Finally, alpha power was negatively associated with the speed of processing in the right frontal lobe, despite the absence of age-related alterations. Our results thus demonstrate that multiple electrophysiological features, as well as their interplay, should be considered when investigating the association between age, neuronal activity, and cognitive performance

Atrial Natriuretic Peptide Levels Reflect Hemodynamic Changes Under Pacemaker Stimulation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73244/1/j.1540-8159.1990.tb02142.x.pd

Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals