Department of Neurosurgery, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
Cereb Cortex. 2022 Dec 8;32(24):5544-5554. doi: 10.1093/cercor/bhac034.
Decoding the inner representation of a word meaning from human cortical activity is a substantial challenge in the development of speech brain-machine interfaces (BMIs). The semantic aspect of speech is a novel target of speech decoding that may enable versatile communication platforms for individuals with impaired speech ability; however, there is a paucity of electrocorticography studies in this field. We decoded the semantic representation of a word from single-trial cortical activity during an imageability-based property identification task that required participants to discriminate between the abstract and concrete words. Using high gamma activity in the language-dominant hemisphere, a support vector machine classifier could discriminate the 2-word categories with significantly high accuracy (73.1 ± 7.5%). Activities in specific time components from two brain regions were identified as significant predictors of abstract and concrete dichotomy. Classification using these feature components revealed that comparable prediction accuracy could be obtained based on a spatiotemporally targeted decoding approach. Our study demonstrated that mental representations of abstract and concrete word processing could be decoded from cortical high gamma activities, and the coverage of implanted electrodes and time window of analysis could be successfully minimized. Our findings lay the foundation for the future development of semantic-based speech BMIs.
从人类皮质活动中解码单词含义的内在表示是语音脑机接口（BMI）发展中的一个重大挑战。语音的语义方面是语音解码的一个新目标，它可能为言语能力受损的个体提供多功能的交流平台；然而，该领域的皮层电图研究还很少。我们在一项基于图像可识别性的属性识别任务中，从单次皮质活动中解码了单词的语义表示，要求参与者区分抽象词和具体词。使用语言优势半球的高伽马活动，支持向量机分类器可以以非常高的准确率（73.1±7.5%）区分这两个词类。两个脑区特定时间成分的活动被确定为抽象和具体二分法的显著预测因子。使用这些特征成分进行分类表明，可以基于时空靶向解码方法获得可比的预测准确性。我们的研究表明，抽象和具体单词处理的心理表示可以从皮质高伽马活动中解码出来，并且可以成功地最小化植入电极的覆盖范围和分析的时间窗口。我们的发现为基于语义的语音 BMI 的未来发展奠定了基础。
