Combining ERPs and EEG spectral features for decoding intended movement direction

The posterior parietal cortex (PPC) plays an important role in visuomotor transformations for movement planning and execution. To investigate how noninvasive electroencephalographic (EEG) signals correlate with intended movement directions in the PPC, this study recorded whole-head EEG during a delayed saccade-or-reach task and found direction-related changes in both event-related potentials (ERPs) and the EEG power in the theta and alpha bands in the PPC. Single-trial (left versus right) classification using ERP and EEG spectral features prior to motor execution obtained an average accuracy of 65.4% and 65.6% respectively on 10 subjects. By combining the two types of features, the classification accuracy increased to 69.7%. These results show that ERP and EEG spectral power modulations contribute complementary information to decoding intended movement directions in the PPC. The proposed paradigm might lead to a practical brain-computer interface (BCI) for decoding movement intention of individuals.