A spectral-ensemble deep random vector functional link network for passive brain–computer interface.

Randomized neural networks (RNNs) have shown outstanding performance in many different fields. The superiority of having fewer training parameters and closed-form solutions makes them popular in small datasets analysis. However, automatically decoding raw electroencephalogram (EEG) data using RNNs is still challenging in EEG-based passive brain–computer interface (pBCI) classification tasks. Models with the high-dimension input of EEG may suffer from overfitting and the intrinsic characteristics of non-stationary, high-level noises and subject variability could limit the generation of distinctive features in the hidden layers. To address these problems in EEG-based pBCI tasks, this work proposes a spectral-ensemble deep random vector functional link (SedRVFL) network that focuses on feature learning in the frequency domain. Specifically, an unsupervised feature-refining (FR) block is proposed to improve the low feature learning capability in RNNs. Moreover, a dynamic direct link (DDL) is performed to further complement the frequency information. The proposed model has been evaluated on a self-collected dataset as well as a public driving dataset. The cross-subject classification results obtained demonstrated its effectiveness. This work offers a new solution for EEG decoding, i.e. , using optimized RNNs for decoding complex raw EEG data and boosting the classification performance of EEG-based pBCI tasks. • A SedRVFL network is proposed for the decoding of high-dimension raw EEG data. • A FR block is presented to improve the feature learning capability of the edRVFL. • Non-linear features are employed in the direct link. • A dynamic direct link algorithm is proposed to reduce the redundant information. [ABSTRACT FROM AUTHOR]