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Decoding the nonstationary neural activity in motor cortex for brain machine interfaces

Previous decoding algorithms used in brain machine interfaces (BMIs) usually seek a static functional mapping between the spatio‐temporal neural activity and behavior and assume that the neural spike statistics do not change over time. However, recent work indicates the significant variance in neural activities, which suggests the nonfeasibility of the stationary assumptions on the neural signal sequences. To track the time‐changing neural activity during the nonlinear decoding process, we developed a time‐varying approach based on general regression neural network (GRNN) with a dynamic pattern layer. Applied on both simulated neural activity and in vivo BMI data extracted from rat's motor cortex, the proposed method reconstructs the movement signals better than the original GRNN algorithm with static pattern layer, which raises the promise of successfully tracking the time‐varying neural activity for BMIs decoding. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 158–164, 2011