Decoding speech from spike-based neural population recordings in secondary auditory cortex of non-human primates.,Communications Biology

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Decoding speech from spike-based neural population recordings in secondary auditory cortex of non-human primates.
Communications Biology ( IF 5.2 ) Pub Date : 2019-12-11 , DOI: 10.1038/s42003-019-0707-9
Christopher Heelan _{1,

2} , Jihun Lee ₁ , Ronan O'Shea ₁ , Laurie Lynch ₁ , David M Brandman ₃ , Wilson Truccolo _{4,

5} , Arto V Nurmikko _{1,

5}

Affiliation

Direct electronic communication with sensory areas of the neocortex is a challenging ambition for brain-computer interfaces. Here, we report the first successful neural decoding of English words with high intelligibility from intracortical spike-based neural population activity recorded from the secondary auditory cortex of macaques. We acquired 96-channel full-broadband population recordings using intracortical microelectrode arrays in the rostral and caudal parabelt regions of the superior temporal gyrus (STG). We leveraged a new neural processing toolkit to investigate the choice of decoding algorithm, neural preprocessing, audio representation, channel count, and array location on neural decoding performance. The presented spike-based machine learning neural decoding approach may further be useful in informing future encoding strategies to deliver direct auditory percepts to the brain as specific patterns of microstimulation.

中文翻译：

从非人类灵长类动物次级听觉皮层中基于尖峰的神经群体记录中解码语音。

与新皮质感觉区域的直接电子通信对于脑机接口来说是一个具有挑战性的目标。在这里，我们报告了首次成功地对英语单词进行神经解码，该解码是根据猕猴次级听觉皮层记录的皮质内尖峰神经群体活动进行的，具有高清晰度。我们使用颞上回（STG）头侧和尾侧副带区域的皮质内微电极阵列获得了 96 通道全宽带群体记录。我们利用新的神经处理工具包来研究解码算法的选择、神经预处理、音频表示、通道数和阵列位置对神经解码性能的影响。所提出的基于尖峰的机器学习神经解码方法可能进一步有助于为未来的编码策略提供信息，以将直接听觉感知作为特定的微刺激模式传递给大脑。

更新日期：2019-12-11

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