Classification of High-Dimensional Motor Imagery Tasks based on An End-to-end role assigned convolutional neural network
This work addresses the challenge of insufficient decoding performance in EEG-based BCIs for users needing intuitive control, though it appears incremental as it builds on existing CNN architectures.
The study tackled the problem of decoding EEG motor imagery signals for brain-computer interfaces by proposing an end-to-end role assigned convolutional neural network (ERA-CNN), which outperformed previous methods on 3-class, 5-class, and 7-class classification tasks with robust performance.
A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. Electroencephalogram (EEG) motor imagery (MI) paradigm is widely used in non-invasive BCI to obtain encoded signals contained user intention of movement execution. However, EEG has intricate and non-stationary properties resulting in insufficient decoding performance. By imagining numerous movements of a single-arm, decoding performance can be improved without artificial command matching. In this study, we collected intuitive EEG data contained the nine different types of movements of a single-arm from 9 subjects. We propose an end-to-end role assigned convolutional neural network (ERA-CNN) which considers discriminative features of each upper limb region by adopting the principle of a hierarchical CNN architecture. The proposed model outperforms previous methods on 3-class, 5-class and two different types of 7-class classification tasks. Hence, we demonstrate the possibility of decoding user intention by using only EEG signals with robust performance using an ERA-CNN.