Research on the application of the improved genetic algorithm in the electroencephalogram-based mental workload evaluation for miners
Electroencephalogram is the electrical phenomena in the cerebral cortex or the scalp surface due to the electrophysiological activity of brain cells. Electroencephalogram has great theoretical and practical significance in measuring mental workload of people. More precise electroencephalographic is...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2016-09-01
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| Series: | Journal of Algorithms & Computational Technology |
| Online Access: | https://doi.org/10.1177/1748301816649071 |
| _version_ | 1818454675409600512 |
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| author | Li Hongxia Di Hongxi Li Jian Tian Shuicheng |
| author_facet | Li Hongxia Di Hongxi Li Jian Tian Shuicheng |
| author_sort | Li Hongxia |
| collection | DOAJ |
| description | Electroencephalogram is the electrical phenomena in the cerebral cortex or the scalp surface due to the electrophysiological activity of brain cells. Electroencephalogram has great theoretical and practical significance in measuring mental workload of people. More precise electroencephalographic is a precondition to study mental workload of miners. In this article, based on the actual situation of the electroencephalographic measurement of miners, the particle swarm optimization is introduced to improve the standard genetic algorithm, and put forward a combined method integrating the genetic algorithm with particle swarm optimization for achieving electroencephalogram-based measures of miners' mental workload. Moreover, the MATLAB simulation platform is used for simulation testing. Testing results prove the effectiveness of the combined method. |
| first_indexed | 2024-12-14T21:58:39Z |
| format | Article |
| id | doaj.art-72b092ca403b47588af35d6c8d22fb96 |
| institution | Directory Open Access Journal |
| issn | 1748-3018 1748-3026 |
| language | English |
| last_indexed | 2024-12-14T21:58:39Z |
| publishDate | 2016-09-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Algorithms & Computational Technology |
| spelling | doaj.art-72b092ca403b47588af35d6c8d22fb962022-12-21T22:46:03ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30181748-30262016-09-011010.1177/1748301816649071Research on the application of the improved genetic algorithm in the electroencephalogram-based mental workload evaluation for minersLi HongxiaDi HongxiLi JianTian ShuichengElectroencephalogram is the electrical phenomena in the cerebral cortex or the scalp surface due to the electrophysiological activity of brain cells. Electroencephalogram has great theoretical and practical significance in measuring mental workload of people. More precise electroencephalographic is a precondition to study mental workload of miners. In this article, based on the actual situation of the electroencephalographic measurement of miners, the particle swarm optimization is introduced to improve the standard genetic algorithm, and put forward a combined method integrating the genetic algorithm with particle swarm optimization for achieving electroencephalogram-based measures of miners' mental workload. Moreover, the MATLAB simulation platform is used for simulation testing. Testing results prove the effectiveness of the combined method.https://doi.org/10.1177/1748301816649071 |
| spellingShingle | Li Hongxia Di Hongxi Li Jian Tian Shuicheng Research on the application of the improved genetic algorithm in the electroencephalogram-based mental workload evaluation for miners Journal of Algorithms & Computational Technology |
| title | Research on the application of the improved genetic algorithm in the electroencephalogram-based mental workload evaluation for miners |
| title_full | Research on the application of the improved genetic algorithm in the electroencephalogram-based mental workload evaluation for miners |
| title_fullStr | Research on the application of the improved genetic algorithm in the electroencephalogram-based mental workload evaluation for miners |
| title_full_unstemmed | Research on the application of the improved genetic algorithm in the electroencephalogram-based mental workload evaluation for miners |
| title_short | Research on the application of the improved genetic algorithm in the electroencephalogram-based mental workload evaluation for miners |
| title_sort | research on the application of the improved genetic algorithm in the electroencephalogram based mental workload evaluation for miners |
| url | https://doi.org/10.1177/1748301816649071 |
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