Classification of Movement Intention Using Independent Components of Premovement EEG - Kim, Hyeonseok; Yoshimura, Natsue; Koike, Yasuharu (Frontiers in Human Neuroscience, 2019)
Many previous studies on brain-machine interfaces (BMIs) have focused on EEG signals elicited during motor-command execution to generate device commands. However, exploiting pre-execution brain activity related to movement intention could improve the practical applicability of BMIs. Therefore, in this study we investigated whether EEG signals occurring before movement execution could be used to classify movement direction. Six subjects performed reaching tasks that required them to move a cursor to one of four targets distributed horizontally and vertically from the center. Using independent components of EEG acquired during a premovement phase, two-class classifications were performed for left versus right trials and top versus bottom trials. Instructions were presented visually (test) and aurally (condition). Classification results showed that a combination of two windows from different time intervals during the premovement phase improved classification performance in the both conditions compared to a single-window classification. By categorizing the independent components according to spatial pattern, we found that information depending on the modality can improve classification performance. We confirmed that EEG signals occurring during movement preparation can be used to control a BMI.