Recent technological advances show the feasibility of offline decoding speech from neuronal signals, paving the way to the development of chronically implanted speech brain computer interfaces (sBCI). Two key steps that still need to be addressed for the online deployment of sBCI are, on the one hand, the definition of relevant design parameters of the recording arrays, on the other hand, the identification of robust physiological markers of the patient’s intention to speak, which can be used to online trigger the decoding process. To address these issues, we acutely recorded speech-related signals from the frontal cortex of two human patients undergoing awake neurosurgery for brain tumors using three different micro-electrocorticographic (μECoG) devices. First, we observed that, at the smallest investigated pitch (600μm), neighboring channels are highly correlated, suggesting that more closely spaced electrodes would provide some redundant information. Second, we trained a classifier to recognize speech-related motor preparation from high-gamma oscillations (70–150Hz), demonstrating that these neuronal signals can be used to reliably predict speech onset. Notably, our model generalized both across subjects and recording devices showing the robustness of its performance. These findings provide crucial information for the design of future online sBCI.

中文翻译：

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通过人脑的微皮质脑电图预测言语开始

最近的技术进步显示了从神经元信号中离线解码语音的可行性，为长期植入语音脑机接口 (sBCI) 的发展铺平了道路。sBCI 的在线部署仍然需要解决的两个关键步骤是，一方面，记录阵列的相关设计参数的定义，另一方面，确定患者说话意图的健壮生理标志物，可用于在线触发解码过程。为了解决这些问题，我们使用三种不同的微皮层电图技术，敏锐地记录了两名接受脑肿瘤清醒神经外科手术的人类患者额叶皮层的语音相关信号。μ心电图）设备。首先，我们观察到，在最小的调查间距（600μm），相邻通道高度相关，表明更紧密间隔的电极将提供一些冗余信息。其次，我们训练了一个分类器从高伽马振荡（70-150Hz），证明这些神经元信号可用于可靠地预测语音开始。值得注意的是，我们的模型在主题和记录设备上进行了概括，显示了其性能的稳健性。这些发现为未来在线 sBCI 的设计提供了重要信息。