Semi-supervised Learning Based EEG Detection Approach for Rehabilitation Engineering

Semi-supervised Learning Based EEG Detection Approach for Rehabilitation Engineering

A semi-supervised learning based EEG signal detection method was studied in this paper. The feature engineering system of this paper was established, which contains novel AutoEncoders mapping features. The optimal channel combination for all subjects was determined to improve recognition accuracy by ReliefF algorithm and recursive feature elimination. What’s more, the semi-supervised learning method based on pseudo-labelling was introduced to the character recognition method, in which the training samples were dynamically reorganized and updated, so that the proposed method could complete the symbol recognition with limited number of training samples. Based on the features extacted and the optimal channel combination, the recognition accuracy of the character recognition method can reach up to 100%.