Jiang Werner, Pailla Tejaswy, Dichter Benjamin, Chang Edward F, Gilja Vikash
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:1532-1535. doi: 10.1109/EMBC.2016.7591002.
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.
脑机接口（BMI）在恢复和辅助瘫痪患者进行交流的应用方面具有巨大潜力。最近，能够解码语音的BMI因其具有高信息传输率的潜力而备受关注。在本研究中，我们提出了一种基于隐马尔可夫模型（HMM）的新型解码方法，该方法利用神经信号变化的时间来解码语音。我们通过预测三名人类受试者的脑皮层电图（ECoG）数据中的元音来测试解码器的性能。我们的结果表明，ECoG信号基于时间的特征能够提供有关元音产生的信息，并使解码准确率显著高于随机水平。这表明利用神经活动的时间结构来解码语音对于开发高性能、稳健的语音BMI可能发挥重要作用。
