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Abstract
Deep Neural Networks have been successful in Brain-Machine Interfaces (BCIs) applications, where they are used to decode mental images from Electroencephalography (EEG) signals. However, each trained model works for a single individual and requires large amounts of data, which is time-consuming and expensive to collect. Using transfer learning, one can train a neural network on a set of s…