Roughness recognition is an important function in the nervous system that facilitates our interactions with the environment. Previous studies have focused on the neuro-cognitive aspects and frequency-based changes in response to the roughness stimuli. In this study, we investigate the effect of different roughness levels on the nonlinear characteristics of EEG signals. Nine healthy subjects participated in the current research and touched three surfaces with different levels of roughness in a passive dynamical way. The experiment was repeated for both hands separately. During the experiment, the EEG signals were recorded. Next, three nonlinear features were extracted using the recurrence quantification analysis (RQA) method; and four classifiers were hired to distinguish six conditions, including three levels of roughness and the touching hand. The results showed that EEG nonlinear characteristics were significantly affected by the variation of surface roughness. The effects were different between touching by the left or the right hand. Moreover, it was observed that employing the RQA-based features leads to the higher accuracy of classification compared to the conventional frequency-based features. Additionally, we found that the brain representation of tactile roughness has a pseudo-random dynamic, and the amount of roughness can influence a network of brain channels. Finally, utilizing the weighted combination of different brain channels while considering the extracted nonlinear features, the LDA classification accuracy was reached 93%. Therefore, it can be suggested that not only temporal variations of brain signals but also their spatial distribution (brain channels) are important to recognize the surface roughness.

粗糙度识别是神经系统中的重要功能，可促进我们与环境的互动。先前的研究集中在神经认知方面以及对粗糙度刺激的响应中基于频率的变化。在这项研究中，我们研究了不同粗糙度水平对脑电信号非线性特征的影响。九名健康受试者参加了当前的研究，并以被动动力方式触摸了三个具有不同粗糙度水平的表面。双手分别重复进行该实验。在实验过程中，记录了脑电信号。接下来，使用递归量化分析（RQA）方法提取了三个非线性特征。雇用了四个分类器来区分六个条件，包括三个级别的粗糙度和手感。结果表明，脑电非线性特性受表面粗糙度的变化影响很大。左手或右手触摸的效果有所不同。此外，已经观察到，与常规的基于频率的特征相比，采用基于RQA的特征导致更高的分类精度。此外，我们发现触觉粗糙度的大脑表示具有伪随机动力学，并且粗糙度的大小会影响大脑通道网络。最后，在考虑提取的非线性特征的同时，利用不同脑通道的加权组合，LDA分类准确率达到了93％。因此，