Optimization of workload level estimation using selection of EEG channel connectivity
| Autor: | Kevin Ardian, Fumihiko Taya, Yu Sun, Anastasios Bezerianos, Tan Kay Chen |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
medicine.diagnostic_test
Computer science Process (engineering) business.industry 05 social sciences 050301 education Workload Electroencephalography Machine learning computer.software_genre Task (project management) medicine 0501 psychology and cognitive sciences 050102 behavioral science & comparative psychology Artificial intelligence business 0503 education computer Communication channel |
| Zdroj: | CEC |
| DOI: | 10.1109/cec.2016.7744031 |
| Popis: | Workload is the amount of cognitive effort executed by a certain subject. Several attempts have been done in order to measure workload level. However, there exists a difficulty in analyzing workload: the problem of individuality, or variability among different individuals and how they respond to similar tasks. In order to have a more objective measure of workload level, the authors employed a more direct analysis upon the system that does cognitive work itself. The use of electroencephalogram (EEG) was employed to measure brain signals and process them to get an objective estimation of workload level. In this study, the authors evaluated the workload level related to complex training-based type task. Piloting simulation task was used to represent such type of task. The authors assess the EEG channel connections and found important connections for the estimation of workload level. This information can be used to build a more an EEG-based workload level estimator that is more efficient, i.e. less channels needed be used to accurately construct the estimation. The authors also found the significant brain signal frequency band that is related to the measure of workload in complex tasks. The problem of individual differences was also resolved using the proposed algorithm. |
| Databáze: | OpenAIRE |
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