Safe Semi-Supervised Extreme Learning Machine for EEG Signal Classification

One major challenge in the current brain-computer interface research is the accurate classification of time-varying electroencephalographic (EEG) signals. The labeled EEG samples are usually scarce, while the unlabeled samples are available in large quantities and easy to collect in real applications. Semisupervised learning (SSL) methods can utilize both labeled and unlabeled data to improve performance over supervised approaches. However, it has been reported that the unlabeled data may undermine the performance of SSL in some cases. To improve the safety of SSL, we proposed a new safety-control mechanism by analyzing the differences between unlabeled data analysis in supervised and semi-supervised learning. We then develop and implement a safe classification method based on the semi-supervised extreme learning machine (SS-ELM). Following this approach, the Wasserstein distance is used to measure the similarities between the predictions obtained from ELM and SS-ELM algorithms, and a different risk degree is thereby calculated for each unlabeled data instance. A risk-based regularization term is then constructed and embedded into the objective function of the SS-ELM. Extensive experiments were conducted using benchmark and EEG datasets to evaluate the effectiveness of the proposed method. Experimental results show that the performance of the new algorithm is comparable to SS-ELM and superior to ELM on average. It is thereby shown that the proposed method is safe and efficient for the classification of EEG signals.