DWCNN_TL

We introduce a parameter-based approach of cross-subject transfer learning for improving the poor-performing individuals in MI-based BCI systems, pooling data from labeled EEG measurements and psychological questionnaires via kernel embedding. To this end, a Deep&Wide neural network for MI classification is implemented to pre-train the network from the source domain. Then, the layer parameter layers are transferred to initialize the target network within a fine-tuning procedure to recompute the Multilayer Perceptron-based accuracy. To perform data fusion combining categorical with the real-valued features, we implement the stepwise kernel matching via Gaussian embedding.