Spalding Z, Duraivel S, Rahimpour S, Wang C, Barth K, Schmitz C, Lad S P, Friedman A H, Southwell D G, Viventi J, Cogan G B
Department of Biomedical Engineering, Duke University, Durham, NC.
Department of Neurosurgery, Duke School of Medicine, Durham, NC.
bioRxiv. 2025 Aug 22:2025.08.21.671516. doi: 10.1101/2025.08.21.671516.
Speech brain-computer interfaces (BCIs) can restore communication in individuals with neuromotor disorders who are unable to speak. However, current speech BCIs limit patient usability and successful deployment by requiring large volumes of patient-specific data collected over long periods of time. A promising solution to facilitate usability and accelerate their successful deployment is to combine data from multiple patients. This has proven difficult, however, due to differences in user neuroanatomy, varied placement of electrode arrays, and sparse sampling of targeted anatomy. Here, by aligning patient-specific neural data to a shared latent space, we show that speech BCIs can be trained on data combined across patients. Using canonical correlation analysis and high-density micro-electrocorticography (μECoG), we uncovered shared neural latent dynamics with preserved micro-scale speech information. This approach enabled cross-patient decoding models to achieve improved accuracies relative to patient-specific models facilitated by the high resolution and broad coverage of μECoG. Our findings support future speech BCIs that are more accurate and rapidly deployable, ultimately improving the quality of life for people with impaired communication from neuromotor disorders.
语音脑机接口（BCIs）能够帮助患有神经运动障碍而无法说话的人恢复沟通能力。然而，目前的语音脑机接口需要长时间收集大量特定患者的数据，这限制了患者的可用性以及其成功应用。一个有助于提高可用性并加速其成功应用的可行解决方案是合并多个患者的数据。然而，由于用户神经解剖结构的差异、电极阵列放置的不同以及目标解剖结构的稀疏采样，事实证明这很困难。在这里，通过将特定患者的神经数据与共享的潜在空间对齐，我们表明语音脑机接口可以在跨患者合并的数据上进行训练。使用典型相关分析和高密度微电极皮层脑电图（μECoG），我们发现了保留微观尺度语音信息的共享神经潜在动态。这种方法使跨患者解码模型相对于由μECoG的高分辨率和广泛覆盖所支持的特定患者模型能够实现更高的准确率。我们的研究结果支持未来更准确、可快速部署的语音脑机接口，最终改善因神经运动障碍导致沟通受损的人们的生活质量。
