Cognitive task analysis and workload classification

Knisely, Benjamin M.; Joyner, Janell S.; Vaughn-Cooke, Monifa

doi:10.1016/j.mex.2021.101235

Cited by 16 publications

(12 citation statements)

References 63 publications

(73 reference statements)

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“…The aim of the study was to build a fast and accurate workload classifier which can be extended to real-time workload classification. Real-time workload classification is an important and very useful cognitive construct for the development of robust BCI systems [ 62 ] and useful in several other domains like Virtual Reality [ 63 ] and Human-Machine Teaming [ 64 ]. In this research, EEG was chosen as the neuroimaging modality with its advantages of being cheap, portable and having high time resolution [ 65 ].…”

Section: Discussionmentioning

confidence: 99%

Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning

Gupta

Siddhad

Pandey

et al. 2021

Sensors

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Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI and other neuroimaging modalities, an efficient method of estimating an individual’s workload using EEG is of paramount importance. Multiple cognitive, psychiatric and behavioral phenotypes have already been known to be linked with “functional connectivity”, i.e., correlations between different brain regions. In this work, we explored the possibility of using different model-free functional connectivity metrics along with deep learning in order to efficiently classify the cognitive workload of the participants. To this end, 64-channel EEG data of 19 participants were collected while they were doing the traditional n-back task. These data (after pre-processing) were used to extract the functional connectivity features, namely Phase Transfer Entropy (PTE), Mutual Information (MI) and Phase Locking Value (PLV). These three were chosen to do a comprehensive comparison of directed and non-directed model-free functional connectivity metrics (allows faster computations). Using these features, three deep learning classifiers, namely CNN, LSTM and Conv-LSTM were used for classifying the cognitive workload as low (1-back), medium (2-back) or high (3-back). With the high inter-subject variability in EEG and cognitive workload and recent research highlighting that EEG-based functional connectivity metrics are subject-specific, subject-specific classifiers were used. Results show the state-of-the-art multi-class classification accuracy with the combination of MI with CNN at 80.87%, followed by the combination of PLV with CNN (at 75.88%) and MI with LSTM (at 71.87%). The highest subject specific performance was achieved by the combinations of PLV with Conv-LSTM, and PLV with CNN with an accuracy of 97.92%, followed by the combination of MI with CNN (at 95.83%) and MI with Conv-LSTM (at 93.75%). The results highlight the efficacy of the combination of EEG-based model-free functional connectivity metrics and deep learning in order to classify cognitive workload. The work can further be extended to explore the possibility of classifying cognitive workload in real-time, dynamic and complex real-world scenarios.

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Section: Discussionmentioning

confidence: 99%

Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning

Gupta

Siddhad

Pandey

et al. 2021

Sensors

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“…And in the electricity company, it relates to controlling and ensuring the smooth distribution of electricity to all areas. The Manager of the control room of each company was involved in the FGD to identify the control room task list based on the task list (Knisely et al, 2021) and determine the task list category based on risk assessment (Saitta et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…Control room jobs involve monitoring and controlling complex systems, such as industrial plants, power grids, traffic management systems, and others (Knisely et al, 2021). The operator in the control room is responsible for monitoring and controlling a large number of variables, such as temperature, pressure, flow rates, and other parameters, to ensure the system is running smoothly and efficiently.…”

Section: Introductionmentioning

confidence: 99%

“…For example, they are equipped with advanced monitoring and control systems that provide operators with realtime feedback and alerts to potential issues. They also provide a rich visual display of information, such as graphs, charts, and video feeds, which help operators to quickly identify patterns and anomalies (Iqbal et al, 2020;Knisely et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Cognitive Fitness of Post-Retirement Employees in Control Room Settings

Damayanti,

Widyanti,

Sari

et al. 2023

Acad. J. Interdiscip. Stud.

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The increasing number of post-retirement workers in the workforce has become a global trend due to efforts to extend the working period after retirement, which is implemented by many countries considering the rapid aging of their populations. While the presence of post-retirement workers can have positive impacts on personal, company, and government levels, they face challenges in interacting with equipment through interfaces that require heavy reliance on cognitive abilities. Cognitive abilities become more important than physical abilities in multitasking jobs that require a lot of interaction with technology, such as control room jobs. These jobs require a high level of attention, concentration, and decision-making ability, making them highly demanding and challenging. This study aims to determine how post-retirement workers meet job demands in the control room, based on identification of critical cognitive abilities. The method employed in this study is divided into two stages. The first stage involves identifying tasks in the control room based on Cognitive Task Analysis (CTA) and identifying critical tasks using risk assessment and Focus Group Discussion (FGD). FGD was conducted on four different types of companies (telecommunication, broadcasting, process industry, and electrical power) that still employ retired workers in the control room jobs. The second stage involves measuring cognitive abilities in 116 individuals aged 56-60 years. The results of CTA indicate there are critical cognitive abilities in control room jobs, such as reasoning, attention, memory, and perception ability. According to the measurement, post-retirement workers (56 - 60 years old) had the problem of improving reasoning, attention, and perceptive ability, despite cognitive capacity measurements remaining in the medium range. In general, post-retirement workers have high memory abilities (above 500), while their abilities in recognition, perception, coordination, and attention are only at 200-400 (a medium level), indicating a decline in cognitive abilities for post-retirement workers. The high memory ability is positive for supporting work in complex systems, while improvement of cognitive abilities is needed for the declining cognitive aspects. Implication of this study were discuss to this paper to consider factors that affect critical cognitive abilities in post-retirement workers when recruiting and training them to work in the control room. Received: 7 May 2023 / Accepted: 28 July 2023 / Published: 5 September 2023

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“…With the advancement of technology, emergency EEG system (eEEG) and wireless microEEG systems are designed by [40] and [41] respectively which are able to extract the brain waves rapidly in emergency departments or in ICU for patients' care and EEG laboratory. Using EEG hyperscanning device, brain signals can be captured simultaneously from multiple subjects which are also useful for non-verbal communication [42].…”

Section: Related Workmentioning

confidence: 99%