Computational Models for Near-Real-Time Performance Predictions Based on Physiological Measures of Workload

Ziegler, Matthias; Russell, Bartlett; Kraft, Amanda; Krein, Michael; Russo, Jon C.; Casebeer, William D.

doi:10.1016/b978-0-12-811926-6.00019-1

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…The combination of several physiological sensors to classify workload states gives better results than using a single one. The approach proposed in [25] combines EEG, ECG, and electrooculography (EOG); and results show the best predictive power for their combination (80%) rather than the analysis of each one independently (70%). In addition, the study in [10] reports an accuracy average of 85.2 (±4.3%) combining EEG, ECG, respiration rate, and EDA to classify four mental states.…”

Section: Related Workmentioning

confidence: 99%

Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals

et al. 2022

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The commercial flightdeck is a naturally multi-tasking work environment, one in which interruptions are frequent come in various forms, contributing in many cases to aviation incident reports. Automatic characterization of pilots’ workloads is essential to preventing these kind of incidents. In addition, minimizing the physiological sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown high correlations with specific cognitive and mental states, such as workload. However, there is not enough evidence in the literature to validate how well models generalize in cases of new subjects performing tasks with workloads similar to the ones included during the model’s training. In this paper, we propose a convolutional neural network to classify EEG features across different mental workloads in a continuous performance task test that partly measures working memory and working memory capacity. Our model is valid at the general population level and it is able to transfer task learning to pilot mental workload recognition in a simulated operational environment.

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Section: Related Workmentioning

confidence: 99%

Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The combination of several physiological sensors to classify workload states gives better results than using a single one. The approach proposed in [24] combines EEG, ECG, and electrooculography (EOG) and results show a highest predictive power for their combination (80%) rather than the analysis of each one independently (70%). Besides, the study in [12] reports an accuracy average of 85.2 (± 4.3%) combining EEG, ECG, respiration rate, and EDA to classify 4 mental states.…”

Section: Related Workmentioning

confidence: 99%

Mental Workload Detection Based on EEG Analysis

Vidalón

Hernàndez-Sabaté

Folch³

et al. 2021

Frontiers in Artificial Intelligence and Applications

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The study of mental workload becomes essential for human work efficiency, health conditions and to avoid accidents, since workload compromises both performance and awareness. Although workload has been widely studied using several physiological measures, minimising the sensor network as much as possible remains both a challenge and a requirement. Electroencephalogram (EEG) signals have shown a high correlation to specific cognitive and mental states like workload. However, there is not enough evidence in the literature to validate how well models generalize in case of new subjects performing tasks of a workload similar to the ones included during model’s training. In this paper we propose a binary neural network to classify EEG features across different mental workloads. Two workloads, low and medium, are induced using two variants of the N-Back Test. The proposed model was validated in a dataset collected from 16 subjects and shown a high level of generalization capability: model reported an average recall of 81.81% in a leave-one-out subject evaluation.

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“…Machine learning can effectively learn unique features that capture inherent patterns in the data and construct predictive models [14]. For instance, a proposed method integrates ECG, EEG, and electrooculography (EOG), demonstrating superior predictive capability compared to individual analyses [15]. Similarly, another research showcases high accuracy by combining ECG, EEG, and respiration rate for the classification of mental conditions [16].…”

Section: Introductionmentioning

confidence: 99%

Exploring Differential Entropy and Multifractal Cumulants for EEG-based Mental Workload Recognition

2024

ijacsa

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In the current research, two nonlinear features were utilized for the design of EEG-based mental workload recognition: one feature based on differential entropy and the other feature based on multifractal cumulants. Clean EEGs recorded from 36 healthy volunteers in both resting and task states were subjected to feature extraction via differential entropy and multifractal cumulants. Then, these nonlinear features were utilized as input for a fuzzy KNN classifier. Experimental results showed that the multifractal cumulants feature vector achieved an AUC of 0.951, which is larger than the differential entropy feature vector (AUC = 0.935). However, the combination of both feature sets resulted in added value in identifying these two mental workloads (AUC = 0.993). Furthermore, the multifractal cumulants feature vector (best classification accuracy = 94.76%) obtained better classification results than the differential entropy feature vector (best classification accuracy = 92.61%). However, the combination of these two feature vectors achieved the best classification results: accuracy of 96.52%, sensitivity of 97.68%, specificity of 95.58%, and F1-score of 96.61%. This shows that these two feature vectors are complementary in identifying different mental workloads.

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Computational Models for Near-Real-Time Performance Predictions Based on Physiological Measures of Workload

Cited by 5 publications

References 8 publications

Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals

Recognition of the Mental Workloads of Pilots in the Cockpit Using EEG Signals

Mental Workload Detection Based on EEG Analysis

Exploring Differential Entropy and Multifractal Cumulants for EEG-based Mental Workload Recognition

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