Reliability of P3 Event-Related Potential during Working Memory Across the Spectrum of Cognitive Aging

Devos, Hannes; Burns, Jeffrey M.; Liao, Ke; Ahmadnezhad, Pedram; Mahnken, Jonathan D.; Brooks, William M.; Gustafson, Kathleen M.

doi:10.1101/2020.05.27.20109157

“…Limitations: Conclusions based on this study should be considered in the context of its limitations. First, only a small number of participants were tested, and the results may not reflect the general population of the older adults [44,50,51]. Second, only the steady state motion patterns without transitions were extracted and analyzed.…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Zhang¹,

Soangra

²

,

Lockhart

³

2020

Sci

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Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

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“…Limitations: Conclusions based on this study should be considered in the context of its limitations. First, only a small number of participants were tested, and the results may not reflect the general population of the older adults [43,49,50]. Second, only the steady state motion patterns without transitions were extracted and analyzed.…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Zhang¹,

Soangra²,

Lockhart³

2020

Sci

0

View full text Add to dashboard Cite

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

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“…These ICC's are in the same range as the reliability of our convergent measure, the P3 ERP component. [9] Comparison of reliability with other measures of pupillary response is complicated by the type of extraction (TEPR versus ICA), the type of task, and the population of interest. The closest comparison is the study by Kahya et al, that estimated reliability of ICA during postural demanding tasks in Parkinson's disease.…”

Section: Discussionmentioning

confidence: 99%

“…Extensive description of the 7-minutes test is provided elsewhere. [9] In short, each n-back test comprised 180 trials, including 60 (33.3%) target trials and 120 (66.7%) nontarget trials. Display time of each letter was 500 ms, followed by a blank interstimulus interval of 1700 ms with a random jitter of 50 ms.…”

Section: N-back Testmentioning

confidence: 99%

“…A previous study by our group found accuracy and response times on the n-back test to be highly reliable performance measures of working memory in older adults. [9] Unlike performance measures, physiological measures can provide a continuous recording of brain activity in real time. Some studies have suggested that physiological changes may appear before manifestation of symptoms in performance measures, thus providing a more sensitive measure of early cognitive decline.…”

Section: Introductionmentioning

confidence: 99%

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Reliability and Validity of Cognitive Workload in Older Adults

Devos

¹

,

Gustafson

²

,

Ahmadnezhad

³

et al. 2020

Preprint

Self Cite

9

0

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Cognitive workload or mental effort is a measure of attention allocation to a task, which can be administered through self-report or physiological measures. Cognitive workload is increasingly recognized as an important determinant of performance in cognitive tests and daily life activities. However, the reliability and validity of these measures have not been established in older adults with a wide range of cognitive ability. The aim of this study was to establish the test-retest reliability of the NASA-Task Load Index (NASA-TLX) and Index of Cognitive Activity (ICA), extracted from pupillary size. The convergent validity of these measures against event-related potentials (ERPs) was also investigated. A total of 38 individuals with scores on the Montreal Cognitive Assessment ranging between 17 and 30 completed a working memory test (n-back) with three levels of difficulty at baseline and two-week follow-up. Intraclass correlation coefficients (ICC) values of the NASA-TLX ranged between 0.71 and 0.81, demonstrating good to excellent reliability. Mean ICA scores showed fair to good reliability, with ICCs ranging between 0.56 and 0.73. Mean ICA and NASA-TLX scores showed significant and moderate correlations with Pearson r ranging between 0.30 and 0.33 with P3 ERP at the midline channels. We conclude that ICC and NASA-TLX are reliable measures of cognitive workload in older adults. Further research is needed in dissecting the subjective and objective constructs of cognitive workload.

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Reliability of P3 Event-Related Potential during Working Memory Across the Spectrum of Cognitive Aging

Cited by 4 publications

References 44 publications

Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Reliability and Validity of Cognitive Workload in Older Adults

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