When brain activity is translated into commands for real applications, the potential for human capacities augmentation is promising. In this paper, EMD is used to decompose EEG signals during Imagined Speech in order to use it as a biometric marker for creating a Biometric Recognition System. For each EEG channel, the most relevant Intrinsic Mode Functions (IMFs) are decided based on the Minkowski distance, and for each IMF 4 features are computed: Instantaneous and Teager energy distribution and Higuchi and Petrosian Fractal Dimension. To test the proposed method, a dataset with 20 subjects who imagined 30 repetitions of 5 words in Spanish, is used. Four classifiers are used for this task - random forest, SVM, naive Bayes, and k-NN - and their performances are compared. The accuracy obtained (up to 0.92 using Linear SVM) after 10-folds cross-validation suggest that the proposed method based on EMD can be valuable for creating EEG-based biometrics of imagined speech for Subjects identification.
翻译:当大脑活动转化为实际应用指令时,增强人的能力的潜力是大有希望的。在本文件中,EMD被用于在想象式演讲中分解 EEG 信号,以用作生物鉴别识别系统创建的生物识别标记。对于每个 EEG 频道,最相关的内在模式函数(IMFs)是根据Minkowski 距离决定的,对于IMF 4 的每个特征都进行计算: 即时和技术能量分布以及Higuchi和Petrosian Fractal 尺寸。为了测试拟议的方法,使用了一套由20个科目组成的数据集,其中的20个科目想象到30个重复了5个西班牙语字。在这项任务中使用了4个分类器 — 随机森林、 SVM、天亮湾和 k-NNN - 及其性能被比较。在10倍交叉校验后获得的精度(使用Linear SVM 达0.92 ) 表明,基于EMD 的拟议方法对于为对象识别目的想象语音创建基于 EEG 的生物鉴别技术很有价值。