Brain-wave recognition of sentences.

Brain-wave recognition of sentences.
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脑电波识别句子。

DOI:
10.1073/pnas.95.26.15861
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发表时间:
1998
影响因子:
11.1
通讯作者:
Zhong
Zhong
中科院分区:
综合性期刊1区
文献类型:
--
作者:
P. Suppes;Bing Han;Zhong

文献摘要

被引文献

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记录两名被试的脑电波和磁波,以识别12个句子或7个单词中的哪一个被加工。分析包括对试验求平均以创建原型和测试样本,对每个样本应用傅立叶变换,然后进行滤波和时域逆变换。使用的过滤器是最佳预测过滤器,为每个主题选择。通过在两个电极的记录之间取得差异以获得双极对,然后将其用于相同的分析,获得了更进一步的改进。基于最小二乘准则的识别率各不相同,但最好的是超过90%。句子原型的第一个词也被剪切和粘贴,以测试,至少部分地,一个词的脑电波在不同的句子上下文中的不变性。最好的结果是80%以上的正确识别。还分析了仅由单个试验组成的供试品。最好的结果是288中的134个正确(47%),这是有希望的,因为预期的偶然识别数只有24(或8.3%)。本文所报道的工作扩展了我们早期仅对单词的脑电波识别的工作。这里报告的识别率进一步加强了这样一种情况,即记录单词或句子的脑电波,加上广泛的数学和统计分析,可以成为我们理解大脑语言处理的新发展的基础。
Electrical and magnetic brain waves of two subjects were recorded for the purpose of recognizing which one of 12 sentences or seven words auditorily presented was processed. The analysis consisted of averaging over trials to create prototypes and test samples, to each of which a Fourier transform was applied, followed by filtering and an inverse transformation to the time domain. The filters used were optimal predictive filters, selected for each subject. A still further improvement was obtained by taking differences between recordings of two electrodes to obtain bipolar pairs that then were used for the same analysis. Recognition rates, based on a least-squares criterion, varied, but the best were above 90%. The first words of prototypes of sentences also were cut and pasted to test, at least partially, the invariance of a word's brain wave in different sentence contexts. The best result was above 80% correct recognition. Test samples made up only of individual trials also were analyzed. The best result was 134 correct of 288 (47%), which is promising, given that the expected recognition number by chance is just 24 (or 8.3%). The work reported in this paper extends our earlier work on brain-wave recognition of words only. The recognition rates reported here further strengthen the case that recordings of electric brain waves of words or sentences, together with extensive mathematical and statistical analysis, can be the basis of new developments in our understanding of brain processing of language.