A Low-rank Spatiotemporal based EEG Multi-Artifacts Cancellation Method for Enhanced ConvNet-DL's Motor Imagery Characterization

• Main Authors: Samuel, Oluwarotimi Williams, Asogbon, Mojisola Grace, Ejay, Esugbe, Geng, Yanjuan, Lopez-Delis, Alberto, Jarrah, Yazan Ali, Idowu, Oluwagbenga Paul, Chen, Shixiong, Fang, Peng, Li, Guanglin
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Brain modeling; Brain-Computer Interfaces; Decoding; Deep learning; Electric potential; Electroencephalography; Filtering; Imagination; Rehabilitation robotics
• ISSN: 2694-0604
• DOI: 10.1109/EMBC46164.2021.9629547

Multi-channel Electroencephalograph (EEG) signal is an important source of neural information for motor imagery (MI) limb movement intent decoding. The decoded MI movement intent often serve as potential control input for brain-computer interface (BCI) based rehabilitation robots. However, the presence of multiple dynamic artifacts in EEG signal leads to serious processing challenge that affects the BCI system in practical settings. Hence, this study propose a hybrid approach based on Low-rank spatiotemporal filtering technique for concurrent elimination of multiple EEG artifacts. Afterwards, a convolutional neural network based deep learning model (ConvNet-DL) that extracts neural information from the cleaned EEG signal for MI tasks decoding was built. The proposed method was studied in comparison with existing artifact removal methods using EEG signals of transhumeral amputees who performed five different MI tasks. Remarkably, the proposed method led to significant improvements in MI task decoding accuracy for the ConvNet-DL model in the range of 8.00~13.98%, while up to 14.38% increment was recorded in terms of the MCC: Mathew correlation coefficients at p