Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems
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Abstract (arXiv)
A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if the model has been trained accurately, it should be able to identify a class and label it accordingly. The CNN model will then be restored and used to try and identify the same class of motor imagery data using much smaller sampled data in an attempt to simulate live data.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 14 references that are in the map. First posted on arXiv 31 May 2022.
Builds on
3 of 14 references.
References · 3
- 2021Safety-enhanced UAV Path Planning with Spherical Vector-based Particle Swarm Optimizationcited by 9
- 2020Motion-Encoded Particle Swarm Optimization for Moving Target Search Using UAVscited by 2
- 2021Hierarchical Convolutional Neural Network with Feature Preservation and Autotuned Thresholding for Crack Detectioncited by 2
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