Word pair classification during imagined speech using direct brain recordings.
Word pair classification during imagined speech using direct brain recordings.
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DOI：
10.1038/srep25803
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发表时间：
2016-05-11
影响因子：
4.6
通讯作者：
Pasley BN
中科院分区：
文献类型：
--
作者：
Martin S;Brunner P;Iturrate I;Millán Jdel R;Schalk G;Knight RT;Pasley BN
由于神经系统疾病而无法交流的人将受益于内部语音解码器。在这里，我们展示了从皮层电图信号中想象语音时对单个单词进行分类的能力。在单词图像任务中，我们使用高伽马（70-150 Hz）时间特征和支持向量机模型对一对单词中的单个单词进行分类。为了解释语音产生过程中的时间不规则性，我们在SVM内核中引入了非线性时间对齐。在两类分类框架（50%的机会水平）中，分类准确率达到88%，并且在5个受试者中，15个词对的平均分类准确率是显著的（平均值= 58%; p < 0.05）。我们还比较了想象的语音，公开的语音和听力之间的分类精度。正如预测的那样，在直接呈现语音刺激的情况下，在听力和明显的语音条件下获得了更高的分类准确率（平均值分别为89%和86%; p < 0.0001）。研究结果为颞叶、额叶和感觉运动皮层中想象单词的神经表征提供了证据，这与先前在言语感知和产生中的发现一致。这些数据代表了语音图像的基本解码的概念研究的证明，并描绘了一些关键的挑战，使用语音图像神经表示的临床应用。
People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70–150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58%; p < 0.05). We also compared classification accuracy between imagined speech, overt speech and listening. As predicted, higher classification accuracy was obtained in the listening and overt speech conditions (mean = 89% and 86%, respectively; p < 0.0001), where speech stimuli were directly presented. The results provide evidence for a neural representation for imagined words in the temporal lobe, frontal lobe and sensorimotor cortex, consistent with previous findings in speech perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications.