Joint spatial-spectral feature space clustering for speech activity detection from ECoG signals.
Joint spatial-spectral feature space clustering for speech activity detection from ECoG signals.
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DOI：
10.1109/tbme.2014.2298897
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发表时间：
2014-04
期刊：
影响因子：
--
通讯作者：
Crone NE
中科院分区：
文献类型：
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作者：
Kanas VG;Mporas I;Benz HL;Sgarbas KN;Bezerianos A;Crone NE
用于语音恢复的脑机接口已经被广泛研究了二十多年。这种系统的成功将部分取决于选择最佳的大脑记录部位和与语音产生相对应的信号特征。本研究的目的是基于ECoG特征空间的联合空间-频率聚类从皮层电图信号中自动检测语音活动。在这项研究中，ECoG信号被记录，而受试者执行两个不同的音节重复任务。我们发现，从ECoG信号中检测语音活动的最佳频率分辨率为8 Hz，采用支持向量机（SVM）作为分类器，准确率达到98.8%。我们还定义了大脑皮层区域，这些区域保存了关于语音和非语音时间间隔的辨别的最多信息。此外，研究结果揭示了与双音节重复任务相关的不同皮层区域，并可能有助于便携式基于ECoG的通信的发展。
Brain machine interfaces for speech restoration have been extensively studied for more than two decades. The success of such a system will depend in part on selecting the best brain recording sites and signal features corresponding to speech production. The purpose of this study was to detect speech activity automatically from electrocorticographic signals based on joint spatial-frequency clustering of the ECoG feature space. For this study, the ECoG signals were recorded while a subject performed two different syllable repetition tasks. We found that the optimal frequency resolution to detect speech activity from ECoG signals was 8 Hz, achieving 98.8% accuracy by employing support vector machines (SVM) as a classifier. We also defined the cortical areas that held the most information about the discrimination of speech and non-speech time intervals. Additionally, the results shed light on the distinct cortical areas associated with the two syllable repetition tasks and may contribute to the development of portable ECoG-based communication.