Brain Center Rudolf Magnus, University Medical Center Utrecht, Dept Neurology and Neurosurgery, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
Brain Center Rudolf Magnus, University Medical Center Utrecht, Dept Neurology and Neurosurgery, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
Neuroimage. 2018 Oct 15;180(Pt A):301-311. doi: 10.1016/j.neuroimage.2017.10.011. Epub 2017 Oct 7.
For people who cannot communicate due to severe paralysis or involuntary movements, technology that decodes intended speech from the brain may offer an alternative means of communication. If decoding proves to be feasible, intracranial Brain-Computer Interface systems can be developed which are designed to translate decoded speech into computer generated speech or to instructions for controlling assistive devices. Recent advances suggest that such decoding may be feasible from sensorimotor cortex, but it is not clear how this challenge can be approached best. One approach is to identify and discriminate elements of spoken language, such as phonemes. We investigated feasibility of decoding four spoken phonemes from the sensorimotor face area, using electrocorticographic signals obtained with high-density electrode grids. Several decoding algorithms including spatiotemporal matched filters, spatial matched filters and support vector machines were compared. Phonemes could be classified correctly at a level of over 75% with spatiotemporal matched filters. Support Vector machine analysis reached a similar level, but spatial matched filters yielded significantly lower scores. The most informative electrodes were clustered along the central sulcus. Highest scores were achieved from time windows centered around voice onset time, but a 500 ms window before onset time could also be classified significantly. The results suggest that phoneme production involves a sequence of robust and reproducible activity patterns on the cortical surface. Importantly, decoding requires inclusion of temporal information to capture the rapid shifts of robust patterns associated with articulator muscle group contraction during production of a phoneme. The high classification scores are likely to be enabled by the use of high density grids, and by the use of discrete phonemes. Implications for use in Brain-Computer Interfaces are discussed.
对于因严重瘫痪或不由自主运动而无法进行交流的人来说，从大脑解码意图言语的技术可能提供了一种替代的交流方式。如果解码被证明是可行的，那么可以开发颅内脑机接口系统，旨在将解码的言语转换为计算机生成的言语或控制辅助设备的指令。最近的进展表明，从感觉运动皮层可能可以进行这种解码，但尚不清楚如何最好地解决这一挑战。一种方法是识别和区分言语的元素，如音素。我们使用高密度电极网格获得的脑电图信号，研究了从感觉运动面部区域解码四个语音音素的可行性。比较了几种解码算法，包括时空匹配滤波器、空间匹配滤波器和支持向量机。使用时空匹配滤波器可将音素正确分类到超过 75%的水平。支持向量机分析达到了类似的水平，但空间匹配滤波器的得分明显较低。最具信息量的电极集中在中央沟沿线。得分最高的是围绕语音起始时间的时间窗口，但起始时间前 500 毫秒的窗口也可以显著分类。结果表明，音素产生涉及皮质表面上一系列稳健且可重复的活动模式。重要的是，解码需要包括时间信息，以捕获与音素产生过程中发音肌群组收缩相关的稳健模式的快速变化。高分类得分可能得益于高密度网格的使用，以及离散音素的使用。讨论了在脑机接口中的应用。
