FIGURE 8.

Workflow of MI data augmentation performance evaluation procedure. The process involves creating a small dataset and assessing accuracy using inverse CV, where one fold is reserved for training and the others for testing. The MI pipeline consists of EEG epoch preprocessing, CSP decomposition, feature extraction, and classification. The NFT-based data augmentation process generates artificial CSP time series using a CTM (Robinson et al., 2002; Robinson et al., 2005; Kerr et al., 2008; Abeysuriya et al., 2015) fitted to the CSP time series from the MI pipeline.