A Jackknife-Inspired Deep Learning Approach to Subject-Independent Classification of EEG

Irina Dolzhikova, Berdakh Abibullaev, Amin Zollanvari

Abstract: Highlights•Proposes DASJ-CNN which is a jackknife-inspired approach with Convolutional Neural Network (CNN) base classifiers.•Presents an analytical motivation of the DASJ ensemble classification.•Evaluates the effectiveness of the method using two state-of-the-art CNN architectures for EEG classification.•Demonstrates the capability of the method to improve the performance of existing CNN architectures.