ArticleSensors (Basel, Switzerland)2019
Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Human-Centric Cognitive State Recognition Using Physiological Signals: A Systematic Review of Machine Learning Strategies Across Application Domains.Sensors (Basel, Switzerland) · 2025Pooled it
- Classification of Motor Functions from Electroencephalogram (EEG) Signals Based on an Integrated Method Comprised of Common Spatial Pattern and Wavelet Transform Framework.Sensors (Basel, Switzerland) · 2019Article
- Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
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%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.