Research on the application of the improved genetic algorithm in the electroencephalogram-based mental workload evaluation for miners

Li, Hongxia; Di, Hongxi; Li, Jian; Tian, Shu

doi:10.1177/1748301816649071

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…From an optimization perspective, algorithms from the Evolutionary Computing (EC) field have been employed as channel reduction or feature selection approaches. Genetic Algorithms (GAs) have been found to be suitable, mostly in wrapper approaches [29][30][31][32]. GAs have also been used to evolve classifiers, like the case of the rule-based Pittsburg-style, where each individual is represented by a variable-length set of rules of the IF-THEN clause type [33].…”

Section: Related Workmentioning

confidence: 99%

EEG Feature Extraction Using Genetic Programming for the Classification of Mental States

et al. 2020

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The design of efficient electroencephalogram (EEG) classification systems for the detection of mental states is still an open problem. Such systems can be used to provide assistance to humans in tasks where a certain level of alertness is required, like in surgery or in the operation of heavy machines, among others. In this work, we extend a previous study where a classification system is proposed using a Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for the classification of two mental states, namely a relaxed and a normal state. Here, we propose an enhanced feature extraction algorithm (Augmented Feature Extraction with Genetic Programming, or +FEGP) that improves upon previous results by employing a Genetic-Programming-based methodology on top of the CSP. The proposed algorithm searches for non-linear transformations that build new features and simplify the classification task. Although the proposed algorithm can be coupled with any classifier, LDA achieves 78.8% accuracy, the best predictive accuracy among tested classifiers, significantly improving upon previously published results on the same real-world dataset.

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Section: Related Workmentioning

confidence: 99%

EEG Feature Extraction Using Genetic Programming for the Classification of Mental States

et al. 2020

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Method for Fault Feature Selection for a Baler Gearbox Based on an Improved Adaptive Genetic Algorithm

Ren

Dong²,

Xue

et al. 2022

Chin. J. Mech. Eng.

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The performance and efficiency of a baler deteriorate as a result of gearbox failure. One way to overcome this challenge is to select appropriate fault feature parameters for fault diagnosis and monitoring gearboxes. This paper proposes a fault feature selection method using an improved adaptive genetic algorithm for a baler gearbox. This method directly obtains the minimum fault feature parameter set that is most sensitive to fault features through attribute reduction. The main benefit of the improved adaptive genetic algorithm is its excellent performance in terms of the efficiency of attribute reduction without requiring prior information. Therefore, this method should be capable of timely diagnosis and monitoring. Experimental validation was performed and promising findings highlighting the relationship between diagnosis results and faults were obtained. The results indicate that when using the improved genetic algorithm to reduce 12 fault characteristic parameters to three without a priori information, 100% fault diagnosis accuracy can be achieved based on these fault characteristics and the time required for fault feature parameter selection using the improved genetic algorithm is reduced by half compared to traditional methods. The proposed method provides important insights into the instant fault diagnosis and fault monitoring of mechanical devices.

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Research on the application of the improved genetic algorithm in the electroencephalogram-based mental workload evaluation for miners

Cited by 2 publications

References 16 publications

EEG Feature Extraction Using Genetic Programming for the Classification of Mental States

EEG Feature Extraction Using Genetic Programming for the Classification of Mental States

Method for Fault Feature Selection for a Baler Gearbox Based on an Improved Adaptive Genetic Algorithm

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