The technology underlying brain computer interfaces has recently undergone rapid development, though a variety of issues remain that are currently preventing it from becoming a viable clinical assistive tool. Though decoding of motor output has been shown to be particularly effective when using spikes, these decoders tend to degrade with the loss of subsets of these signals. One potential solution to this problem is to include features derived from LFP signals in the decoder to mitigate these negative effects. We explored this solution and found that the decline in decoding performance that accompanies spiking unit dropout was significantly reduced when LFP power features were included in the decoder. Additionally, high frequency LFP features in the 100-170 Hz band were more effective than low frequency LFP features in the 2-4 Hz band at protecting the decoder from a dropoff in performance. LFP power appears to be an effective signal to improve the robustness of spiking unit decoders. Future studies will explore online classification and performance improvements in chronic implants by the proposed method.