Effects of Cognitive Distraction on Upper Limb Movement Decoding From EEG Signals

Hand movement decoding from electroencephalograms (EEG) signals is vital to the rehabilitation and assistance of upper limb-impaired patients. Few existing studies on hand movement decoding from EEG signals consider any distractions. However, in practice, patients can be distracted while using the hand movement decoding systems in real life. In this paper, we aim to investigate the effects of cognitive distraction on movement decoding performance.

We first propose a robust decoding method of hand movement directions to cognitive distraction from EEG signals by using the Riemannian Manifold to extract affine invariant features and Gaussian Naive Bayes classifier (named RM-GNBC). Then, we use the experimental and simulated EEG data under conditions without and with distraction to compare the decoding performance of three decoding methods (including the proposed method, tangent space linear discriminant analysis (TSLDA), and baseline method)).

The simulation and experimental results show that the Riemannian-based methods (i.e., RM-GNBC and TSLDA) have higher accuracy under the conditions without and with cognitive distraction and smaller decreases in decoding accuracy between the conditions without and with cognitive distraction than the baseline method. Furthermore, the RM-GNBC method has 6% (paired t-test, p = 0.026) and 5% (paired t-test, p = 0.137) higher accuracies than the TSLDA method under the conditions without and with cognitive distraction, respectively.

The results show that the Riemannian-based methods have higher robustness to cognitive distraction.

This work contributes to developing a brain-computer interface (BCI) to improve the rehabilitation and assistance of hand-impaired patients in real life and open an avenue to the studies on the effects of distraction on other BCI paradigms.