Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems

Alessandro Gallo; Manh Duong Phung

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arXiv · 2206.07655

Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems

Alessandro Gallo·Manh Duong Phung

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Abstract

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.

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BibTeX RIS

Alessandro Gallo, Manh Duong Phung. 2022-05-31. Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems. https://arxiv.org/abs/2206.07655

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