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

• Main Authors: Li, Ruilin, Gao, Ruobin, Suganthan, Ponnuthurai Nagaratnam, Cui, Jian, Sourina, Olga, Wang, Lipo
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; Ensemble deep random vector functional link; Electroencephalogram
• Online Access: https://hdl.handle.net/10356/174550
• Institution: Nanyang Technological University

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.