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首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >Single-trial classification of vowel speech imagery using common spatial patterns.
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Single-trial classification of vowel speech imagery using common spatial patterns.

机译:使用常见的空间模式对元音语音图像进行单次尝试分类。

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摘要

With the goal of providing a speech prosthesis for individuals with severe communication impairments, we propose a control scheme for brain-computer interfaces using vowel speech imagery. Electroencephalography was recorded in three healthy subjects for three tasks, imaginary speech of the English vowels /a/ and /u/, and a no action state as control. Trial averages revealed readiness potentials at 200 ms after stimulus and speech related potentials peaking after 350 ms. Spatial filters optimized for task discrimination were designed using the common spatial patterns method, and the resultant feature vectors were classified using a nonlinear support vector machine. Overall classification accuracies ranged from 68% to 78%. Results indicate significant potential for the use of vowel speech imagery as a speech prosthesis controller.
机译:为了为严重交流障碍的人提供语音假体,我们提出了一种使用元音语音图像的脑机接口控制方案。在三个健康受试者中记录了脑电图,以完成三个任务,即英语元音/ a /和/ u /的虚构语音,以及无动作状态作为对照。试验平均值显示刺激后200毫秒的准备就绪电位,而与语音相关的电位在350毫秒后达到峰值。使用常见的空间模式方法设计了用于任务识别的优化空间滤波器,然后使用非线性支持向量机对所得特征向量进行分类。总体分类准确度从68%到78%不等。结果表明使用元音语音图像作为语音假体控制器的巨大潜力。

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