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      EEG-Based Neurocognitive Metrics May Predict Simulated and On-Road Driving Performance in Older Drivers

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          Abstract

          The number of older drivers is steadily increasing, and advancing age is associated with a high rate of automobile crashes and fatalities. This can be attributed to a combination of factors including decline in sensory, motor, and cognitive functions due to natural aging or neurodegenerative diseases such as HIV-Associated Neurocognitive Disorder (HAND). Current clinical assessment methods only modestly predict impaired driving. Thus, there is a need for inexpensive and scalable tools to predict on-road driving performance. In this study EEG was acquired from 39 HIV+ patients and 63 healthy participants (HP) during: 3-Choice-Vigilance Task (3CVT), a 30-min driving simulator session, and a 12-mile on-road driving evaluation. Based on driving performance, a designation of Good/Poor (simulator) and Safe/Unsafe (on-road drive) was assigned to each participant. Event-related potentials (ERPs) obtained during 3CVT showed increased amplitude of the P200 component was associated with bad driving performance both during the on-road and simulated drive. This P200 effect was consistent across the HP and HIV+ groups, particularly over the left frontal-central region. Decreased amplitude of the late positive potential (LPP) during 3CVT, particularly over the left frontal regions, was associated with bad driving performance in the simulator. These EEG ERP metrics were shown to be associated with driving performance across participants independent of HIV status. During the on-road evaluation, Unsafe drivers exhibited higher EEG alpha power compared to Safe drivers. The results of this study are 2-fold. First, they demonstrate that high-quality EEG can be inexpensively and easily acquired during simulated and on-road driving assessments. Secondly, EEG metrics acquired during a sustained attention task (3CVT) are associated with driving performance, and these metrics could potentially be used to assess whether an individual has the cognitive skills necessary for safe driving.

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          Electrical signs of selective attention in the human brain.

          Auditory evoked potentials were recorded from the vertex of subjects who listened selectively to a series of tone pips in one ear and ignored concurrent tone pips in the other ear. The negative component of the evoked potential peaking at 80 to 110 milliseconds was substantially larger for the attended tones. This negative component indexed a stimulus set mode of selective attention toward the tone pips in one ear. A late positive component peaking at 250 to 400 milliseconds reflected the response set established to recognize infrequent, higher pitched tone pips in the attended series.
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            Diagnosis of Alzheimer's disease from EEG signals: where are we standing?

            This paper reviews recent progress in the diagnosis of Alzheimer's disease (AD) from electroencephalograms (EEG). Three major effects of AD on EEG have been observed: slowing of the EEG, reduced complexity of the EEG signals, and perturbations in EEG synchrony. In recent years, a variety of sophisticated computational approaches has been proposed to detect those subtle perturbations in the EEG of AD patients. The paper first describes methods that try to detect slowing of the EEG. Next the paper deals with several measures for EEG complexity, and explains how those measures have been used to study fluctuations in EEG complexity in AD patients. Then various measures of EEG synchrony are considered in the context of AD diagnosis. Also the issue of EEG pre-processing is briefly addressed. Before one can analyze EEG, it is necessary to remove artifacts due to for example head and eye movement or interference from electronic equipment. Pre-processing of EEG has in recent years received much attention. In this paper, several state-of-the-art pre-processing tech- niques are outlined, for example, based on blind source separation and other non-linear filtering paradigms. In addition, the paper outlines opportunities and limitations of computational approaches for diagnosing AD based on EEG. At last, future challenges and open problems are discussed.
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              EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks.

              The ability to continuously and unobtrusively monitor levels of task engagement and mental workload in an operational environment could be useful in identifying more accurate and efficient methods for humans to interact with technology. This information could also be used to optimize the design of safer, more efficient work environments that increase motivation and productivity. The present study explored the feasibility of monitoring electroencephalo-graphic (EEG) indices of engagement and workload acquired unobtrusively and quantified during performance of cognitive tests. EEG was acquired from 80 healthy participants with a wireless sensor headset (F3-F4,C3-C4,Cz-POz,F3-Cz,Fz-C3,Fz-POz) during tasks including: multi-level forward/backward-digit-span, grid-recall, trails, mental-addition, 20-min 3-Choice Vigilance, and image-learning and memory tests. EEG metrics for engagement and workload were calculated for each 1 -s of EEG. Across participants, engagement but not workload decreased over the 20-min vigilance test. Engagement and workload were significantly increased during the encoding period of verbal and image-learning and memory tests when compared with the recognition/ recall period. Workload but not engagement increased linearly as level of difficulty increased in forward and backward-digit-span, grid-recall, and mental-addition tests. EEG measures correlated with both subjective and objective performance metrics. These data in combination with previous studies suggest that EEG engagement reflects information-gathering, visual processing, and allocation of attention. EEG workload increases with increasing working memory load and during problem solving, integration of information, analytical reasoning, and may be more reflective of executive functions. Inspection of EEG on a second-by-second timescale revealed associations between workload and engagement levels when aligned with specific task events providing preliminary evidence that second-by-second classifications reflect parameters of task performance.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                15 January 2019
                2018
                : 12
                : 532
                Affiliations
                [1] 1Advanced Brain Monitoring Inc. , Carlsbad, CA, United States
                [2] 2Systems Technology, Inc. , Hawthorne, CA, United States
                [3] 3Department of Psychiatry, University of California, San Diego , San Diego, CA, United States
                Author notes

                Edited by: Karel Brookhuis, University of Groningen, Netherlands

                Reviewed by: Jodi M. Gilman, Massachusetts General Hospital, Harvard Medical School, United States; Berry Wijers, University of Groningen, Netherlands

                *Correspondence: Greg Rupp grupp@ 123456b-alert.com
                Article
                10.3389/fnhum.2018.00532
                6341028
                4388fa62-dc0f-4911-a6f7-f3e2b290deb2
                Copyright © 2019 Rupp, Berka, Meghdadi, Karić, Casillas, Smith, Rosenthal, McShea, Sones and Marcotte.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 June 2018
                : 17 December 2018
                Page count
                Figures: 10, Tables: 3, Equations: 0, References: 94, Pages: 14, Words: 10884
                Categories
                Neuroscience
                Original Research

                Neurosciences
                eeg,event related potentials,sustained attention,driving,hiv,neurodegeneration,driving impairment test,on-road evaluation

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