Working Memory Ability Evaluation Based on Fuzzy Support Vector Regression
Author:
Affiliation:
1. Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan
2. Department of Gerontology and Health Care Management, Chang Gung University of Science and Technology, Taoyuan City 33303, Taiwan
Abstract
Funder
National Science and Technology Council
Publisher
MDPI AG
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Link
https://www.mdpi.com/1424-8220/23/19/8246/pdf
Reference20 articles.
1. Charbonnier, S., Roy, R., Doležalová, R., Campagne, A., and Bonnet, S. (2016). Estimation of Working Memory Load Using EEG Connectivity Measures, Conference of Biosignals.
2. Kuanar, S., Athitsos, V., Pradhan, N., Mishra, A., and Rao, K.R. (2018, January 15–20). Cognitive Analysis of Working Memory Load from EEG, by A Deep Recurrent Neural Network. Proceedings of the IEEE International Conference on Acoustics, Calgary, AB, Canada.
3. Barua, S., Ahmed, M.U., and Begum, S. (2020). Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification. Brain Sci., 10.
4. Automatic Eyeblink and Muscular Artifact Detection and Removal From EEG Signals Using k-Nearest Neighbor Classifier and Long Short-Term Memory Networks;Ghosh;IEEE Sens. J.,2023
5. From Regional to Global Brain: A Novel Hierarchical Spatial-Temporal Neural Network Model for EEG Emotion Recognition;Li;IEEE Trans. Affect. Comput.,2022
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