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首页> 外文会议>International IEEE/EMBS Conference on Neural Engineering >Time-frequency analysis of EEG signals from healthy subjects allocated by gender for a subject-independent BCI-based on motor imagery
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Time-frequency analysis of EEG signals from healthy subjects allocated by gender for a subject-independent BCI-based on motor imagery

机译：基于运动图像的健康个体脑电信号的时频分析，该信号按性别分配给与个体无关的BCI

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摘要

Most of the recent brain-computer interfaces (BCI) based on motor imagery are designed by taking into account a single user. A BCI system designed for multiple users without the need of extensive training sessions could be a viable solution for the system's implementation outside research centers. The present work explores the design of an independent-subject BCI. In order to accomplish this, Linear Discriminant Analysis classifiers were designed with the data of a sample of 30 healthy volunteers as a whole group and separated by gender. Three different methods were employed to compute power spectrum features from the volunteer's electroencephalographic recordings. The results show that it is possible to design an independent-subject BCI for the classification of right or left hand motor imagery with respect of a reference interval with classification accuracies above 70%. The female gender could benefit more from a subject-independent classifier, than the male gender.
机译：最近基于运动图像的大多数脑机接口（BCI）都是通过考虑单个用户来设计的。为多个用户而设计的BCI系统，而无需进行广泛的培训，可能是该系统在研究中心之外实施的可行解决方案。目前的工作探讨了独立科目BCI的设计。为了实现这一目标，设计了线性判别分析分类器，将30名健康志愿者的样本数据作为一个整体，并按性别进行了分类。三种不同的方法被用来从志愿者的脑电图记录中计算功率谱特征。结果表明，有可能设计一个独立的对象BCI来对参考精度为70％以上的参考间隔进行左右手运动图像的分类。与男性相比，女性可以从独立于主题的分类器中受益更多。

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