EEG-Based Estimation of Mental Fatigue: Convergent Evidence for a Three-State Model

Trejo, Leonard J.; Knuth, Kevin H.; Prado, Raquel; Rosipal, Roman; Kubitz, Karla A.; Kochavi, Rebekah; Matthews, Bryan; Zhang, Yuzheng

doi:10.1007/978-3-540-73216-7_23

Cited by 60 publications

(36 citation statements)

References 15 publications

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“…Moreover, many other studies have been performed to model brain activity during mental fatigue based on time and frequency features extracted from EEG signals [11][12][13][14][15][16][17]. In these studies, clustering methods were used to indicate brain states during fatiguing processes [18].…”

Section: Introductionmentioning

confidence: 99%

Evaluating nonlinear variability of mental fatigue behavioral indices during long‐term attentive task

et al. 2012

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This study investigates the behavioral indices of attention. A simple repetitive attentive task that resulted in mental fatigue was used consecutively in four trials. In the first step, reaction time and error responses were recorded to evaluate differences among trials. During the task, subjects showed different responses to stimulations. In the second part, to recognize the strategies, multiple clustering methods such as k-means and fuzzy c-means were performed in which behavioral indices and nonlinear features were used. In the last section, mental behavior was identified as a result of the chaotic properties of variations in reaction time. Therefore, the Lyapunov exponent of reaction times was evaluated. Results revealed that behavioral indices could distinguish attention from the occurrence of mental fatigue in trials. In addition, the three strategies used by subjects during the test protocol were assessed. Finally, variation of indices extracted from nonlinear analysis, that is, decrease in degree of chaotic behavior determined the transition from attention to mental fatigue.

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Section: Introductionmentioning

confidence: 99%

Evaluating nonlinear variability of mental fatigue behavioral indices during long‐term attentive task

et al. 2012

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“…We tested two types of atomic decomposition, each of which identifies unique EEG sources simultaneously in three dimensions: 1) atoms with dimensions of power spectral density, space (electrode position), and time (time on task or task conditions), or 2) atoms with dimensions of magnitude squared coherence, spatial relationships (electrode pairs), and time. For tasks that induced mental workload, we found atoms that combine sources in the theta (4)(5)(6)(7)(8) and alpha (8)(9)(10)(11)(12) Hz) EEG frequency bands consistently in individual participants at different times of day and on different days. The temporal variations of the atoms clearly reflected the levels of mental workload induced by varying task conditions.…”

Section: Physiological … And/or Cortical/cognitive Mechanisms Undementioning

confidence: 91%

“…seconds of EEG data, a robust multivariate algorithm correctly identified 90 to 100% of periods during which individuals performing a demanding 3-hour task experienced cognitive fatigue. 5,6 A PDT variant of this algorithm accurately classified fatigue in Air Force pilots over a 37-hr vigil and proved to be resistant to noise and reduced sensor density. 16 Other NASA research had shown that real-time feedback derived from physiological measures of mental engagement prolonged or enhanced human performance during supervisory control of automated systems.…”

Section: Physiological … And/or Cortical/cognitive Mechanisms Undementioning

confidence: 99%

“…For example, certain kernel partial least squares (KPLS) algorithms classified physiological states corresponding to states of mental engagement or cognitive fatigue using a few seconds of EEG and other signals with 90-100% accuracy and good test-retest reliability. 4,5,6 However, algorithms to estimate cognitive workload are typically only about 60-70% accurate under realistic test conditions. 7,8 For example, Pacific Development and Technology (PDT) recently applied a KPLS algorithm to estimate engagement and workload for the US Army Toxins II SBIR program.…”

Section: Introductionmentioning

confidence: 99%

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Advanced Physiological Estimation of Cognitive Status (APECS)

Trejo¹

2009

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information.Send comments regarding this burden estimate or any other aspect of this collection of information, including suggesstions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA, 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any oenalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. EEG (electroencephalogram) recorded in human participants as they performed tasks that induced different mental states, including engagement, mental workload, and mental fatigue. We tested two types of atomic decomposition, each of which identifies unique EEG sources simultaneously in three dimensions: 1) atoms with dimensions of power spectral density, space (electrode position), and time (time on task or task conditions), or 2) atoms with dimensions of magnitude squared coherence, spatial relationships (electrode pairs), and time. For tasks that induced REPORT DATE (DD-MM-YYYY)4. TITLE AND SUBTITLE 13-11-2009 13. SUPPLEMENTARY NOTES The views, opinions and/or findings contained in this report are those of the author(s) and should not contrued as an official Department of the Army position, policy or decision, unless so designated by other documentation. DISTRIBUTION AVAILIBILITY STATEMENT (d) ManuscriptsNumber of Peer-Reviewed Conference Proceeding publications (other than abstracts): 1 Number of Manuscripts:0.00 Number of Inventions:Graduate Students PERCENT_SUPPORTED NAME FTE Equivalent:Total Number: Names of Post DoctoratesPERCENT_SUPPORTED NAME FTE Equivalent:Total Number: Names of Faculty SupportedPERCENT_SUPPORTED NAME FTE Equivalent:Total Number: Names of Under Graduate students supportedPERCENT_SUPPORTED NAME FTE Equivalent:Total Number:The number of undergraduates funded by this agreement who graduated during this period with a degree in science, mathematics, engineering, or technology fields:The number of undergraduates funded by your agreement who graduated during this period and will continue to pursue a graduate or Ph.D. degree in science, mathematics, engineering, or technology fields:Number of graduating undergraduates who achieved a 3.5 GPA to 4.0 (4.0 max scale): Number of graduating undergraduates funded by a DoD funded Center of Excellence grant for Education, Research and Engineering:The number of undergraduates funded by your agreement who graduated during this period and intend to work for the Department of DefenseThe number of undergraduates funded by your agreement who graduated during this period and will receive ...

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“…However, there is contradictory evidence whether such performance measures accurately render the fatigue process. In contrast, neurophysiology based measures can provide an objective and direct characterization of the driver's cognitive state with high temporal resolution (Lin et al, 2005;Trejo et al, 2007;Shen et al, 2008). Currently, advances in technology and in signal processing enable real-time measurements of drivers' cognitive states under real traffic conditions (Kohlmorgen et al, 2007;Dixon et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Assessing Drivers’ Fatigue State Under Real Traffic Conditions Using EEG Alpha Spindles

Schrauf

Simon

Schmidt

et al. 2011

Proceedings of the 6th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design : Dr

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Summary:The effectiveness of EEG alpha spindles, defined by short narrowband bursts in the alpha band, as an objective measure for assessing driver fatigue under real driving conditions was examined using an algorithm for the identification of alpha spindles. The method is applied to data recorded under real traffic conditions and compared with the performance of the traditional EEG fatigue measure alpha band power. Statistical analysis revealed significant increases from the first to the last driving section of alpha band power; with larger effect sizes for the alpha spindle based measures. An increased level of fatigue for drop-outs, as compared to participants who did not abort the drive, was observed only by means of alpha spindle parameters. EEG alpha spindle parameters increase both fatigue detection sensitivity and specificity as compared to EEG alpha band power. It is demonstrated that alpha spindles are superior to EEG band power measures for assessing driver fatigue under real traffic conditions.

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EEG-Based Estimation of Mental Fatigue: Convergent Evidence for a Three-State Model

Cited by 60 publications

References 15 publications

Evaluating nonlinear variability of mental fatigue behavioral indices during long‐term attentive task

Evaluating nonlinear variability of mental fatigue behavioral indices during long‐term attentive task

Advanced Physiological Estimation of Cognitive Status (APECS)

Assessing Drivers’ Fatigue State Under Real Traffic Conditions Using EEG Alpha Spindles

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