A new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. Paper: https://doi.org/10.1088/1741-2552/ac4430
In order to achieve real-time classification, we are working on a new strategy:
Instead of creating a sliding window, the new strategy is to split the epoch into 2 (for now) and then train the net with a double number of examples. This implies no incoherent sample where there is more than one possible label.
In order to achieve real-time classification, we are working on a new strategy: Instead of creating a sliding window, the new strategy is to split the epoch into 2 (for now) and then train the net with a double number of examples. This implies no incoherent sample where there is more than one possible label.