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EEG Representations of Spatial and Temporal Features in Imagined Speech and Overt Speech

机译:想象语音和公开语音中时空特征的脑电图表征

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Imagined speech is an emerging paradigm for intuitive control of the brain-computer interface based communication system. Although the decoding performance of the imagined speech is improving with actively proposed architectures, the fundamental question about 'what component are they decoding?' is still remaining as a question mark. Considering that the imagined speech refers to an internal mechanism of producing speech, it may naturally resemble the distinct features of the overt speech. In this paper, we investigate the close relation of the spatial and temporal features between imagined speech and overt speech using electroencephalography signals. Based on the common spatial pattern feature, we acquired 16.2% and 59.9% of averaged thirteen-class classification accuracy (chance rate = 7.7%) for imagined speech and overt speech, respectively. Although the overt speech showed significantly higher classification performance compared to the imagined speech, we found potentially similar common spatial pattern of the identical classes of imagined speech and overt speech. Furthermore, in the temporal feature, we examined the analogous grand averaged potentials of the highly distinguished classes in the two speech paradigms. Specifically, the correlation of the amplitude between the imagined speech and the overt speech was 0.71 in the class with the highest true positive rate. The similar spatial and temporal features of the two paradigms may provide a key to the bottom-up decoding of imagined speech, implying the possibility of robust classification of multiclass imagined speech. It could be a milestone to comprehensive decoding of the speech-related paradigms, considering their underlying patterns.
机译:想象的语音是对基于脑机接口的通信系统进行直观控制的新兴范例。尽管通过主动提出的体系结构,可以想象的语音的解码性能有所提高,但是有关“它们解码的是什么分量”的基本问题。仍然保留为问号。考虑到想象中的语音是指产生语音的内部机制,它自然可能类似于公开语音的独特特征。在本文中,我们使用脑电图信号研究了想象的语音和明显的语音之间的时空特征的紧密关系。基于常见的空间模式特征,对于假想语音和公开语音,我们分别获得了平均13类分类准确率的16.2%和59.9%(机会率= 7.7%)。尽管公开语音与想象语音相比显示出明显更高的分类性能,但我们发现了相同类别的想象语音和公开语音的潜在相似公共空间模式。此外,在时间特征中,我们检查了两种语音范式中高度杰出的类的相似的平均平均电位。具体而言,在真实阳性率最高的类别中,想象的语音和明显的语音之间的幅度的相关性是0.71。两个范例的相似时空特征可以为虚拟语音的自底向上解码提供关键,这暗示了对多类虚拟语音进行鲁棒分类的可能性。考虑到它们的基本模式,这可能是对语音相关范例进行全面解码的里程碑。

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