Decoding of Grasp Motions from EEG Signals Based on a Novel Data Augmentation Strategy
This work addresses improving BCI systems for clinical applications like neural prostheses, though it is incremental with specific gains in accuracy.
The study tackled decoding grasp motions from EEG signals by proposing a novel data augmentation method using EMG labels, resulting in average classification accuracies of 52.49% for motor execution and 40.36% for motor imagery, which are 9.30% and 6.19% higher than comparable methods.
Electroencephalogram (EEG) based brain-computer interface (BCI) systems are useful tools for clinical purposes like neural prostheses. In this study, we collected EEG signals related to grasp motions. Five healthy subjects participated in this experiment. They executed and imagined five sustained-grasp actions. We proposed a novel data augmentation method that increases the amount of training data using labels obtained from electromyogram (EMG) signals analysis. For implementation, we recorded EEG and EMG simultaneously. The data augmentation over the original EEG data concluded higher classification accuracy than other competitors. As a result, we obtained the average classification accuracy of 52.49% for motor execution (ME) and 40.36% for motor imagery (MI). These are 9.30% and 6.19% higher, respectively than the result of the comparable methods. Moreover, the proposed method could minimize the need for the calibration session, which reduces the practicality of most BCIs. This result is encouraging, and the proposed method could potentially be used in future applications such as a BCI-driven robot control for handling various daily use objects.