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

<p dir="ltr">Randomized <u>neural networks</u> (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 <u>electroencephalogram</u> (EEG) data using RNNs is still challenging in EEG-based passive brain–computer interface (pBCI) <u>classification tasks</u>. 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 <u>feature learning</u> capability in RNNs. Moreover, a dynamic direct link (DDL) is performed to further complement the <u>frequency information</u>. The proposed model has been evaluated on a self-collected dataset as well as a public driving dataset. The cross-subject <u>classification results</u> 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.</p><h2>Other Information</h2><p dir="ltr">Published in: Expert Systems with Applications<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.eswa.2023.120279" target="_blank">https://dx.doi.org/10.1016/j.eswa.2023.120279</a></p>