Brain-machine interfaces (BMIs) have great potential for applications that restore and assist communication for paralyzed individuals. Recently, BMIs decoding speech have gained considerable attention due to their potential for high information transfer rates. In this study, we propose a novel decoding approach based on hidden Markov models (HMMs) that uses the timing of neural signal changes to decode speech. We tested the decoder's performance by predicting vowels from electrocorticographic (ECoG) data of three human subjects. Our results show that timing-based features of ECoG signals are informative of vowel production and enable decoding accuracies significantly above the level of chance. This suggests that leveraging the temporal structure of neural activity to decode speech could play an important role towards developing highperformance, robust speech BMIs.
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