A cross-attention swin transformer network for EEG-based subject-independent cognitive load assessment
Author:
Funder
STI 2030-Major Projects
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11571-024-10160-7.pdf
Reference49 articles.
1. Asgher U, Khalil K, Khan MJ et al (2020) Enhanced accuracy for multiclass mental workload detection using long short-term memory for brain-computer interface. Front Neurosci 14:584. https://doi.org/10.3389/fnins.2020.00584
2. Aziz S, Khan MU, Aamir F, Javid MA (2019) Electromyography (EMG) data-driven load classification using empirical mode decomposition and feature analysis. In: 2019 International conference on frontiers of information technology. https://doi.org/10.1109/FIT47737.2019.00058
3. Belkhiria C, Peysakhovich V (2021) EOG metrics for cognitive workload detection. Procedia Comput Sci 192:1875–1884. https://doi.org/10.1016/j.procs.2021.08.193
4. Bethge D, Hallgarten P, Grosse-Puppendahl T, et al (2022) Domain-invariant representation learning from EEG with private encoders. In: IEEE, Singapore, Singapore, pp 1236–1240. https://doi.org/10.1109/ICASSP43922.2022.9747398
5. Born J, Brn R, Sa RP, et al (2019) Multimodal study of the effects of varying task load utilizing EEG, GSR and eye-tracking. Cold Spring Harbor Laboratory. https://doi.org/10.1101/798496
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