Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, USA.
Nat Commun. 2022 Jan 10;13(1):48. doi: 10.1038/s41467-021-27725-3.
Reconstructing intended speech from neural activity using brain-computer interfaces holds great promises for people with severe speech production deficits. While decoding overt speech has progressed, decoding imagined speech has met limited success, mainly because the associated neural signals are weak and variable compared to overt speech, hence difficult to decode by learning algorithms. We obtained three electrocorticography datasets from 13 patients, with electrodes implanted for epilepsy evaluation, who performed overt and imagined speech production tasks. Based on recent theories of speech neural processing, we extracted consistent and specific neural features usable for future brain computer interfaces, and assessed their performance to discriminate speech items in articulatory, phonetic, and vocalic representation spaces. While high-frequency activity provided the best signal for overt speech, both low- and higher-frequency power and local cross-frequency contributed to imagined speech decoding, in particular in phonetic and vocalic, i.e. perceptual, spaces. These findings show that low-frequency power and cross-frequency dynamics contain key information for imagined speech decoding.
使用脑机接口从神经活动中重建意图言语，为严重言语生成障碍的人带来了巨大的希望。虽然解码显性言语已经取得了进展，但解码想象中的言语却遇到了有限的成功，主要是因为与显性言语相比，相关的神经信号较弱且变化较大，因此难以通过学习算法进行解码。我们从 13 名患者中获得了三个脑电数据集，这些患者因癫痫评估而植入电极，他们执行显性和想象中的言语产生任务。基于最近的言语神经处理理论，我们提取了一致且特定的神经特征，可用于未来的脑机接口，并评估了它们在发音、语音和元音表示空间中区分言语项的性能。虽然高频活动为显性言语提供了最佳信号，但低频和高频功率以及局部跨频都有助于想象中的言语解码，特别是在语音和元音，即感知空间中。这些发现表明，低频功率和跨频动态包含想象中的言语解码的关键信息。
