ISSN   1004-0595

CN  62-1224/O4

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陈思, 陈晨, 乔筱祺, 李阔, 茹伟民, 王冬青, 唐玮. 基于EEG-fNIRS技术的纹理触觉感知研究[J]. 摩擦学学报, 2023, 43(12): 1416-1425. DOI: 10.16078/j.tribology.2022215
引用本文: 陈思, 陈晨, 乔筱祺, 李阔, 茹伟民, 王冬青, 唐玮. 基于EEG-fNIRS技术的纹理触觉感知研究[J]. 摩擦学学报, 2023, 43(12): 1416-1425. DOI: 10.16078/j.tribology.2022215
CHEN Si, CHEN Chen, QIAO Xiaoqi, LI Kuo, RU Weimin, WANG Dongqing, TANG Wei. Texture Tactile Perception Based on EEG-fNIRS Technology[J]. TRIBOLOGY, 2023, 43(12): 1416-1425. DOI: 10.16078/j.tribology.2022215
Citation: CHEN Si, CHEN Chen, QIAO Xiaoqi, LI Kuo, RU Weimin, WANG Dongqing, TANG Wei. Texture Tactile Perception Based on EEG-fNIRS Technology[J]. TRIBOLOGY, 2023, 43(12): 1416-1425. DOI: 10.16078/j.tribology.2022215

基于EEG-fNIRS技术的纹理触觉感知研究

Texture Tactile Perception Based on EEG-fNIRS Technology

  • 摘要: 纹理触觉感知是指尖与物体接触摩擦时大脑所产生的物体表面特征随时间空间变化的感觉. 在滑动摩擦过程中,纹理表面的微观几何形状会在皮肤中引起微小的变形和振动,被编码为神经冲动传递到神经中枢,人们以此来区分表面细微的纹理特征. 本文中利用脑电图-功能近红外光谱(EEG-fNIRS)同步联合成像技术,搭建了多模态摩擦触觉感知测试平台,设计了不同纹理特征的样本,用于观察指尖摩擦振动诱发的大脑生理活动. 摩擦振动试验结果表明声音信号的功率谱重心与摩擦系数近似呈负相关,可以反映试验样本的摩擦学行为;声音信号的梅尔频率倒谱系数(MFCC)的灰度直方图和分形维数等统计特征可以反应样本的纹理特征. EEG-fNIRS同步联合试验结果表明不同纹理表面主要激活左脑区域,共同激活的通道以中央后回为主,其中初级躯体感觉皮层SI的触觉响应最显著. 手指触摸样本过程中,α节律为主要脑电成分,能量占比约45%,其次是δ节律和θ节律,能量占比约25%. 脑电信号的样本熵和排序熵等特征参数不能很好地表征本试验样本的差异性.

     

    Abstract: Texture features play a crucial role in object recognition. Although texture can be perceived through both visual and tactile perspectives, the sense of touch dominates in perceiving material properties such as texture. Tactile perception of texture is the sensation of spatial changes in object surface features over time that occurs in the brain when the tip rubs against the object. During sliding friction, the microscopic geometry of the textured surface causes small deformations and vibrations in the skin, which are encoded as nerve impulses transmitted to the nerve center, and people use this to distinguish subtle textural features of the surface. In this paper, a multimodal frictional tactile perception test platform was built using simultaneous EEG-fNIRS imaging, and two groups of samples with different texture characteristics were designed, namely, the changing texture spacing group and the changing texture depth group, and then the frictional vibration and the induced physiological brain activity during fingertip contact with the frictional samples were observed to further investigate the human tactile perception of different textures and the formation mechanism of tactile perception of textured surfaces. The results of the friction vibration experiment showed that the fitted curve of the sample friction coefficient roughly passed the zero point, and only slight adhesion occurred between the sample and the finger, which was the tribological behavior dominated by shear force; the power spectrum center of gravity of the sound signal was approximately negatively correlated with the friction coefficient, which can reflect the tribological behavior of the experimental sample; The Mayer frequency cepstrum coefficient (MFCC) can reflect the static characteristics of the sound signal, and the results showed that the statistical features such as gray histogram and fractal dimension of MFCC can reflect the texture characteristics of this experimental sample. The results of the joint EEG-fNIRS synchronization experiment showed that touching different textural features on the surface mainly activated left brain regions, mainly because the body complied with contralateral administration, while right-handed touch was used in this experiment; the co-activated channels were dominated by the postcentral gyrus, with the most significant tactile response in primary somatosensory cortex SI. In the process of finger touch sample induced brain activity, alpha rhythm is the main EEG component, which is related to human cognitive function with about 45% of energy, followed by delta rhythm and theta rhythm with about 25% of energy, and there is no significant difference between left and right brain regions in comparison (P>0.05), which shows that both left and right brain regions are involved in tactile perception. The characteristic parameters such as sample entropy and sorting entropy of EEG signals cannot characterize the variability of the samples in this experiment well, mainly because the samples in this experiment are more finely textured and subtle differences are difficult to be distinguished.

     

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