Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria.
Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria.
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神经假体用于解码的Anarthria的瘫痪者。
DOI：
10.1056/nejmoa2027540
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发表时间：
2021-07-15
期刊：
影响因子：
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通讯作者：
Chang EF
中科院分区：
文献类型：
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作者：
Moses DA;Metzger SL;Liu JR;Anumanchipalli GK;Makin JG;Sun PF;Chartier J;Dougherty ME;Liu PM;Abrams GM;Tu-Chan A;Ganguly K;Chang EF
恢复瘫痪患者不能说话的交流能力的技术有可能提高自主性和生活质量。一种直接从这类患者的大脑皮层活动中解码单词和句子的方法，可能代表着相对于现有的辅助交流方法的进步。我们在感觉运动皮质区域植入了硬膜下高密度多电极阵列，用于控制因脑干中风导致的构音障碍(发音能力丧失)和痉挛四肢瘫痪患者的言语。在48个疗程中，我们记录了22个小时的大脑皮质活动，而参与者试图从50个单词中说出单个单词。我们使用深度学习算法来创建计算模型，用于从记录的皮层活动中的模式中检测和分类单词。我们应用这些计算模型，以及一个自然语言模型，在参与者试图说出完整的句子时，在给定序列中前面的单词的情况下产生下一个单词的概率。我们从参与者的大脑皮层活动中实时解码句子，中位数为每分钟15.2个单词，中位数错误率为25.6%。在事后分析中，我们检测到98%的参与者试图产生单独的单词，并使用在81周的研究期间稳定的大脑皮质信号对单词进行分类，准确率为47.1%。在一名由脑干中风引起的构音障碍和痉挛四肢瘫痪的患者中，使用深度学习模型和自然语言模型，在试图说话的过程中，直接从皮质活动中解码单词和句子。(由Facebook和其他公司资助；ClinicalTrials.gov编号，NCT03698149。)
Technology to restore the ability to communicate in paralyzed persons who cannot speak has the potential to improve autonomy and quality of life. An approach that decodes words and sentences directly from the cerebral cortical activity of such patients may represent an advancement over existing methods for assisted communication. We implanted a subdural, high-density, multielectrode array over the area of the sensorimotor cortex that controls speech in a person with anarthria (the loss of the ability to articulate speech) and spastic quadriparesis caused by a brain-stem stroke. Over the course of 48 sessions, we recorded 22 hours of cortical activity while the participant attempted to say individual words from a vocabulary set of 50 words. We used deep-learning algorithms to create computational models for the detection and classification of words from patterns in the recorded cortical activity. We applied these computational models, as well as a natural-language model that yielded next-word probabilities given the preceding words in a sequence, to decode full sentences as the participant attempted to say them. We decoded sentences from the participant’s cortical activity in real time at a median rate of 15.2 words per minute, with a median word error rate of 25.6%. In post hoc analyses, we detected 98% of the attempts by the participant to produce individual words, and we classified words with 47.1% accuracy using cortical signals that were stable throughout the 81-week study period. In a person with anarthria and spastic quadriparesis caused by a brain-stem stroke, words and sentences were decoded directly from cortical activity during attempted speech with the use of deep-learning models and a natural-language model. (Funded by Facebook and others; ClinicalTrials.gov number, NCT03698149.)