Typing with ten fingers on a virtual keyboard in virtual or augmented reality exposes a challenging input interpretation problem. There are many sources of noise in this interaction context and these exacerbate the challenge of accurately translating human actions into text. A particularly challenging input noise source arises from the physiology of the hand. Intentional finger movements can produce unintentional coactivations in other fingers. On a physical keyboard, the resistance of the keys alleviates this issue. On a virtual keyboard, coactivations are likely to introduce spurious input events under a naive solution to input detection. In this paper we examine the features that discriminate intentional activations from coactivations. Based on this analysis, we demonstrate three alternative coactivation detection strategies with high discrimination power. Finally, we integrate coactivation detection into a probabilistic decoder and demonstrate its ability to further reduce uncorrected character error rates by approximately 10% relative and 0.9% absolute.