Decoding motion direction using the topography of sustained ERPs and alpha oscillations.

The present study sought to determine whether scalp electroencephalogram (EEG) signals contain decodable information about the direction of motion in random dot kinematograms (RDKs), in which the motion information is spatially distributed and mixed with random noise. Any direction of motion from 0 to 360° was possible, and observers reported the precise direction of motion at the end of a 1500-ms stimulus display. We decoded the direction of motion separately during the motion period (during which motion information was being accumulated) and the report period (during which a shift of attention was necessary to make a fine-tuned direction report). Machine learning was used to decode the precise direction of motion (within ±11.25°) from the scalp distribution of either alpha-band EEG activity or sustained event-related potentials (ERPs). We found that ERP-based decoding was above chance (1/16) during both the stimulus and the report periods, whereas alpha-based decoding was above chance only during the report period. Thus, sustained ERPs contain information about spatially distributed direction-of-motion, providing a new method for observing the accumulation of sensory information with high temporal resolution. By contrast, the scalp topography of alpha-band EEG activity appeared to mainly reflect spatially focused attentional processes rather than sensory information.