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

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

URL: http://arxiv.org/abs/2206.07655v1
Date: Tue, 31 May 2022 17:09:46 GMT
Title: Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems
Authors: Alessandro Gallo and Manh Duong Phung
Abstract summary: A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery. 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.
Score: 79.58173794910631
License: http://creativecommons.org/licenses/by/4.0/
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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