Diagnostic Monitoring Of Vigilance Decrement Using EEG Workload Indices

Kamzanova, Altyngul; Kustubayeva, Almira; Matthews, Gerald

doi:10.1177/1071181312561019

Cited by 10 publications

(6 citation statements)

References 13 publications

(43 reference statements)

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“…Critical signals for detection were cases in which both of the RPAs vectored toward the center of the sector on a potential collision path in either the northwest to southeast or the northeast to southwest heading. In all experimental conditions, the display was updated 30 times/minute with a dwell time of 80 ms (see Kamzanova, Kustubayeva, & Matthews, 2012). For each participant, 5 critical signals occurred at random intervals in each flight path heading during each period of watch (overall signal probability = 3.33%).…”

Section: Vigilance Taskmentioning

confidence: 99%

Vigilance

Dillard

Warm

Funke

et al. 2013

Proceedings of the Human Factors and Ergonomics Society Annual

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This study tested the possibility that the temporal context in which a vigilance task is performed will moderate the perceived workload of the task. We employed a procedure to manipulate participants’ perceived time progression (PTP) during task performance by creating a mismatch between their expectations about how long they would perform the task and the actual time they were engaged (Sackett et al., 2010). All participants worked at the task for 30 minutes. Those in a time drags condition were led to believe it would last 15 minutes while those in a time flies condition were told it would last 60 minutes. PTP was significantly slower in the former condition than in the latter. However, workload scores on the NASA Task Load Index were similar in the two conditions and fell at the upper level of the scale. Evidently, vigilance tasks are perceived as hard work even when time flies.

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Section: Vigilance Taskmentioning

confidence: 99%

Vigilance

Dillard

Warm

Funke

et al. 2013

Proceedings of the Human Factors and Ergonomics Society Annual

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“…The Lane Change Test (LCT) version 1.2 simulator was used in the experiment, simulating a vehicle’s most frequent driving conditions [ 49 ]. LCT is designed to quantitatively measure the level of degradation of the subject’s performance while driving and performing other secondary tasks [ 42 , 43 , 44 ].…”

Section: Experimentation and Materialsmentioning

confidence: 99%

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Becerra-Sánchez

Reyes-Muñoz

Ibáñez

2020

Sensors

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In recent years, research has focused on generating mechanisms to assess the levels of subjects’ cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools for analyzing the cognitive workload, and electroencephalographic (EEG) signals have been most frequently used due to their high precision. However, one of the main challenges in implementing the EEG signals is finding appropriate information for identifying cognitive states. Here, we present a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workloads and structures a new dataset capable of optimizing the model’s predictive process. We found that GALoRIS identifies data related to high and low cognitive workloads of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50% and maximizing the model’s predictive capacity, achieving a precision rate greater than 90%.

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“…Furthermore, the APF has been linked to cognitive abilities (Kamzanova et al, 2012, 2020, Kustubayeva et al, 2013, 2022. Grandy et al (2013) found that individual APF was related to latent factors of general cognitive abilities, indicating a potential association between alpha oscillations and cognitive performance.…”

Section: Introductionmentioning

confidence: 99%

Machine learning algorithms for age prediction based on linear and non-linear parameters of electroencephalogram data

Sadibekov,

Zhulduzbaev,

Merkibek

et al. 2024

BIO Web Conf.

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Gaining insights into cognitive and behavioral changes during childhood and adolescence requires a fundamental understanding of the developmental trajectory of the human brain. This research aimed to predict the age of children using linear and non-linear measures of baseline electroencephalogram (EEG) data. EEG is a method that records the electrical activity of the brain, providing valuable insights into its functioning. Participants were 182 children between 7 to 20 years old. Peak alpha and entropy were correlated with age. Various machine learning models were implemented, with Decision Trees yielding the best results. The Decision Trees model achieved strong correlation between predicted and actual age. The study demonstrated the stability of age prediction error over time, suggesting individual brain maturational levels. The findings highlight the potential of EEG data for accurate age prediction, providing insights into brain maturation patterns. This research contributes to tracking neurodevelopment and understanding brain function across age groups, including typically developing children.

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Diagnostic Monitoring Of Vigilance Decrement Using EEG Workload Indices

Cited by 10 publications

References 13 publications

Vigilance

Vigilance

Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers

Machine learning algorithms for age prediction based on linear and non-linear parameters of electroencephalogram data

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