Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems
This work addresses EEG classification for brain-computer interfaces, but it is incremental as it uses an existing method on new data without reporting specific performance metrics.
The study applied a pre-trained CNN to classify motor imagery from EEG data for brain-computer interfaces, achieving successful identification with pre-processed data and testing on smaller samples to simulate live conditions.
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.