Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 4; p. 808
• Main Authors: Zhang, Yihong, Shen, Yuou
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 16.02.2019; MDPI AG
• Subjects: cognitive workload; electroencephalography; feature enhancement; mental workload; neural network; spectral map; cognitive workload; electroencephalography; feature enhancement; spectral map; neural network; mental workload
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19040808

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%.