EEG signal processing, for brain computer interface (BCI) with Deep Motor Feature

In this paper we present multi-scale deep convolutional neural networks to work in Electroencephalography (EEG) signals for imagined motor. We have some suggestion for the learning of a set of high-level feature representations by deep learning which is purposed as Deep Motor Features (DeepMF), that is used in brain computer interface (BCI) with imagined motor tasks. Since the extracted DeepMF is different for various tasks and similar for the same tasks. So it is convenient to separate different EEG signals for imagined motor tasks apart. Our approach achieves 100% accuracy for 4 classes imagined motor EEG signals classification on Project BCI - EEG motor activity dataset. Moreover, thanks to the highly abstract features DeepMF learned, only 4.125 seconds trials of training data are needed. This DeepMF is compared with the conventional BLDA (Bayesian Linear Discriminant Analysis ) algorithm for 8.75 seconds trials to achieve the same accuracy. Accordingly the BCI response time and the required trials for training are almost declined by half. The experiments are provided to illustrate the effectiveness of the proposed design approach