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首页> 外文会议>Mediterranean Conference on Medical and Biological Engineering and Computing >Voice Activity Detection from Electrocorticographic Signals
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Voice Activity Detection from Electrocorticographic Signals

机译：从电加电信号检测语音活动

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The purpose of this study was to explore voice activity detection (VAD) in a subject with implanted electrocorticographic (ECoG) electrodes. Accurate VAD is an important preliminary step before decoding and reconstructing speech from ECoG. For this study we used ECoG signals recorded while a subject performed a picture naming task. We extracted time-domain features from the raw ECoG and spectral features from the ECoG high gamma band (70-110Hz). The Re-lieF algorithm was used for selecting a subset of features to use with seven machine learning algorithms for classification. With this approach we were able to detect voice activity from ECoG signals, achieving a high accuracy using the 100 best features from all electrodes (96%) or only 12 features from the two best electrodes (94%) using the support vector machines or a linear regression classifier. These findings may contribute to the development of ECoG-based brain machine interface (BIY1I) systems for rehabilitating individuals with communication impairments.
机译：本研究的目的是探讨具有植入电阻（ECOG）电极的受试者的语音活动检测（VAD）。准确的VAD是解码和重建ECOG的语音之前的重要初步步骤。对于本研究，我们使用拍摄对象的eCog信号进行图片命名任务。我们从ECOG高伽马带（70-110Hz）中提取了从原始ECOG和光谱功能的时间域特征。 RE-Lief算法用于选择用于分类的七种机器学习算法的特征子集。通过这种方法，我们能够使用来自所有电极（96％）的100个最佳功能（96％）或使用来自两个最佳电极（94％）的最佳特征来检测来自ECOG信号的语音活动，从而使用两个最佳电极（94％）使用支持向量机或a线性回归分类器。这些发现可能有助于开发基于ECOG的脑机接口（BIY1i）系统，用于恢复具有通信障碍的个人。

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