Spatial Functional Data and Classification Techniques to Control a Hand Prosthesis Using Silent Speech

Abstract We propose a novel alternative to generate and classify electroencephalography images, involving the spatial correlation structure among the functional brain-signals. We combine spatial functional methods with image classification techniques to carry out supervised classification of a new silent speech thought, in one specific option that determines an action of a hand prosthesis. Using functional kriging, we find the optimized prediction of the brain signals on unsampled brain locations. Based on these spatial functional predictions, we build a sequence of images in the frequency domain, and then we apply machine learning and deep learning algorithms to this sequence of images. The proposal is applied to a dataset of electroencephalography signals collected on the language area of the brain while people are thinking in silence on each of the five vowels in the Spanish language. Our methodology is implemented in real-life computer interface machines using Google Compute Engine, testing the period for the machine calibration and training the classification algorithm.