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RSS FeedsSensors, Vol. 19, Pages 808: Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification (Sensors)

 
 

18 february 2019 23:01:24

 
Sensors, Vol. 19, Pages 808: Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification (Sensors)
 


Electroencephalography (EEG) provides a non-invasive, portable and low-cost way to convert neural signals into electrical signals. Using EEG to monitor people’s cognitive workload means a lot, especially for tasks demanding high attention. Before deep neural networks became a research hotspot, the use of spectrum information and the common spatial pattern algorithm (CSP) was the most popular method to classify EEG-based cognitive workloads. Recently, spectral maps have been combined with deep neural networks to achieve a final accuracy of 91.1% across four levels of cognitive workload. In this study, a parallel mechanism of spectral feature-enhanced maps is proposed which enhances the expression of structural information that may be compressed by inter- and intra-subject differences. A public dataset and milestone neural networks, such as AlexNet, VGGNet, ResNet, DenseNet are used to measure the effectiveness of this approach. As a result, the classification accuracy is improved from 91.10% to 93.71%.


 
69 viewsCategory: Chemistry, Physics
 
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