Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Department of Neurosurgery, Duke School of Medicine, Durham, NC, USA.
Nat Commun. 2023 Nov 6;14(1):6938. doi: 10.1038/s41467-023-42555-1.
Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse neural recordings which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed high-resolution, micro-electrocorticographic (µECoG) neural recordings during intra-operative speech production. We obtained neural signals with 57× higher spatial resolution and 48% higher signal-to-noise ratio compared to macro-ECoG and SEEG. This increased signal quality improved decoding by 35% compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than linear techniques. We show that high-density µECoG can enable high-quality speech decoding for future neural speech prostheses.
患有使人衰弱的神经退行性疾病的患者通常会失去沟通能力，这对他们的生活质量造成了不利影响。一种恢复沟通的方法是直接从大脑解码信号，从而实现神经语音假体。然而，由于神经记录不够精细，无法充分捕捉人类大脑信号的丰富时空结构，因此解码一直受到限制。为了解决这一限制，我们在术中进行语音产生期间进行了高分辨率微脑电描记术 (µECoG) 神经记录。与宏观 ECoG 和 SEEG 相比，我们获得了具有 57 倍更高空间分辨率和 48%更高信噪比的神经信号。与标准颅内信号相比，这种更高的信号质量将解码提高了 35%。准确的解码取决于神经接口的高空间分辨率。旨在利用增强的时空神经信息的非线性解码模型产生了比线性技术更好的结果。我们表明，高密度µECoG 可以为未来的神经语音假体实现高质量的语音解码。
