Abstract:[Objective] Although tactile perception plays a crucial role in human perception of the external world, human understanding of tactile perception remains limited due to the complexity of its mechanism and the multitude of perceptual units involved. The friction between surface textures and finger skin provides vibratory stimuli on the skin surface during tactile perception, thereby activating the somatosensory areas. It is necessary to evaluate the tactile perception of fine textures based on the friction behavior of skin and the related cortical activity in response to the texture stimuli.[Methods] Different fine stripe texture depths (5, 10, 15, 20, 25, and 30 μm) were designed and processed using laser engraving. The depth recognition threshold of tactile perception for fine texture was systematically investigated using subjective evaluation, surface friction and vibration, and the neurophysiological response of the brain. The effects of the texture stimulus intensity and neuronal excitability on tactile perception were verified by a single-channel neural mass model.[Results] An increase in the fine texture depth was associated with an increase in the subjective human texture sense, the degree of correct texture recognition, and the proportion of deformation friction. The average depth recognition threshold of tactile perception was found to be 11.60 μm. The load index, the maximum spectral amplitude of the vibration signal, the recurrence parameter entropy, the length of the longest vertical line segment, and the peak of P300 exhibited a substantial positive correlation with the fine texture depth. The latency of P300 showed a substantial negative correlation with the fine texture depth. When the texture depth exceeded the depth recognition threshold of tactile perception, the maximum spectral amplitude and nonlinear characteristic parameters of the touch vibration signal increased remarkably. The main frequency of the vibration signal also increased to be within the perceptual frequency range of the Pacinian corpuscle. As a result, the vibration signal system transformed from a homogenous state to a disrupted state. Furthermore, the intensity and the area of activation of the brain regions, the neuronal activity of the brain, the processing intensity, and the tactile recognition speed of the brain increased remarkably. Amplitude of the main frequency of the simulated electroencephalogram (EEG) signal increased with an increase in the mean value of the input signal. This trend was consistent with that of the real EEG signal, which indicated that the increase in the tactile intensity due to the increase in the texture depth was one of the reasons for the increase in amplitude of the main frequency of the tactile EEG signal. The main frequency of the simulated EEG signal decreased with an increase in the ratio of excitatory synaptic gain to inhibitory synaptic gain. This trend was consistent with that of the real EEG signal, which indicated that the increased excitability of the neuronal populations excited by the increase in texture depth was one of the reasons for the decrease in the main frequency of the tactile EEG signal.[Conclusions] The depth recognition thresholds of tactile perception for fine stripe textures, the finger touch tribological behavior, the frequency domain and nonlinear features of the touch vibration signals, and the time and frequency domain features of EEG signals undergo remarkable variations during touching and sensing. The single-channel neural mass model can effectively simulate real EEG signals.
[1] CESINI I, NDENGUE J D, CHATELET E, et al. Correlation between friction-induced vibrations and tactile perception during exploration tasks of isotropic and periodic textures[J]. Tribology International, 2018, 120:330-339. [2] DALLMANN C J, ERNST M O, MOSCATELLI A. The role of vibration in tactile speed perception[J]. Journal of Neurophysiology, 2015, 114(6):3131-3139. [3] 周丽丽, 姚欣茹, 汤征宇, 等. 触觉信息处理及其脑机制[J]. 科技导报, 2017, 35(19):37-43. ZHOU L L, YAO X R, TANG Z Y, et al. Neural mechanisms of tactile information processing[J]. Science & Technology Review, 2017, 35(19):37-43. (in Chinese) [4] LEDERMAN S J, KLATZKY R L. Haptic perception:A tutorial[J]. Attention, Perception, & Psychophysics, 2009, 71(7):1439-1459. [5] HOLLINS M, BENSMAÏA S J. The coding of roughness[J]. Canadian Journal of Experimental Psychology/Revue Canadienne De Psychologie Expérimentale, 2007, 61(3):184-195. [6] KATZ D. The world of touch[M]. New York:Psychology Press, 2013. [7] BELL J, BOLANOWSKI S, HOLMES M H. The structure and function of Pacinian corpuscles:A review[J]. Progress in Neurobiology, 1994, 42(1):79-128. [8] VERRILLO R T. Effect of contactor area on the vibrotactile threshold[J]. The Journal of the Acoustical Society of America, 1963, 35(12):1962-1966. [9] BENSMAÏA S, HOLLINS M. Pacinian representations of fine surface texture[J]. Perception & Psychophysics, 2005, 67(5):842-854. [10] 高凌云. 指纹能增强触觉[J]. 现代物理知识, 2009, 21(4):65. GAO L Y. Fingerprints enhance the sense of touch[J]. Modern Physics, 2009, 21(4):65. (in Chinese) [11] SCHEIBERT J, LEURENT S, PREVOST A, et al. The role of fingerprints in the coding of tactile information probed with a biomimetic sensor[J]. Science, 2009, 323(5920):1503-1506. [12] VALLBO A B, JOHANSSON R S. Properties of cutaneous mechanoreceptors in the human hand related to touch sensation[J]. Human Neurobiology, 1984, 3(1):3-14. [13] JOHNSON K O. The roles and functions of cutaneous mechanoreceptors[J]. Current Opinion in Neurobiology, 2001, 11(4):455-461. [14] ODDO C M, BECCAI L, WESSBERG J, et al. Roughness encoding in human and biomimetic artificial touch:Spatiotemporal frequency modulation and structural anisotropy of fingerprints[J]. Sensors, 2011, 11(6):5596-5615. [15] CRAMPHORN L, WARD-CHERRIER B, LEPORA N F. Addition of a biomimetic fingerprint on an artificial fingertip enhances tactile spatial acuity[J]. IEEE Robotics and Automation Letters, 2017, 2(3):1336-1343. [16] 唐玮, 张梅梅, 杨雷, 等. 粗糙表面的摩擦触觉感知研究[J]. 摩擦学学报, 2022, 42(4):764-774. TANG W, ZHANG M M, YANG L, et al. Tactile perception of rough surface using friction and electroencephalography methods[J]. Tribology, 2022, 42(4):764-774. (in Chinese) [17] 唐玮, 张梅梅, 刘瑞, 等. 不同尖锐度纹理形状的摩擦触觉感知与表征研究[J]. 摩擦学学报, 2021, 41(3):373-381. TANG W, ZHANG M M, LIU R, et al. Tactile perception of texture shape with diferent sharpness from finger friction to brain a ctivation[J]. Tribology, 2021, 41(3):373-381. (in Chinese) [18] OLAUSSON H, WESSBERG J, KAKUDA N. Tactile directional sensibility:Peripheral neural mechanisms in man[J]. Brain Research, 2000, 866(1):178-187. [19] 李炜, 蒋玉石, 庞强, 等. 不同性别人体皮肤摩擦不舒适度感知功能的研究[J]. 摩擦学学报, 2012, 32(3):227-232. LI W, JIANG Y S, PANG Q, et al. Discomfort perception on human skin among different gender during friction contact[J].Tribology, 2012, 32(3):227-232. (in Chinese) [20] 古斯塔夫·费希纳. 心理物理学纲要[M]. 李晶, 译. 北京:中国人民大学出版社, 2015. FECHNER G T. Outline of psychophysics[M]. LI J, Trans. Beijing:China Renmin University Press, 2015. (in Chinese) [21] POLICH J, MARGALA C. P300 and probability:Comparison of oddball and single-stimulus paradigms[J]. International Journal of Psychophysiology, 1997, 25(2):169-176. [22] 左雪. 磨损表面形貌的分形表征及其随磨损过程的变化规律研究[D]. 徐州:中国矿业大学, 2017. ZUO X. Fractal characterization of worn surface topography and its variation during wear process[D]. Xuzhou:China University of Mining and Technology, 2017. (in Chinese) [23] 贾强辉. 递归图在非平稳信号中的应用分析[D]. 合肥:合肥工业大学, 2019. JIA Q H. Application and analysis of recurrence plot in non-stationary signals[D]. Heifei:Hefei University of Technology, 2019. (in Chinese) [24] 杨栋. 桥梁健康监测信号的递归特性分析[D]. 长沙:中南大学, 2012. YANG D. Analysis of recurrence characteristics for the monitoring signals of bridge SHM[D]. Changsha:Central South University, 2012. (in Chinese) [25] WEBBER C L JR, MARWAN N. Recurrence quantification analysis:Theory and best practices[M]. Cham:Springer, 2015. [26] 孙国栋. 磨合吸引子表征及预测建模研究[D]. 徐州:中国矿业大学, 2019. SUN G D. Characterization and prediction modeling of running-in attractors[D]. Xuzhou:China University of Mining and Technology, 2019. (in Chinese) [27] STERIADE M. Grouping of brain rhythms in corticothalamic systems[J]. Neuroscience, 2006, 137(4):1087-1106. [28] CANNON J, MCCARTHY M M, LEE S, et al. Neurosystems:Brain rhythms and cognitive processing[J]. European Journal of Neuroscience, 2014, 39(5):705-719. [29] LYTTON W W. Computer modelling of epilepsy[J]. Nature Reviews Neuroscience, 2008, 9(8):626-637. [30] JANSEN B H, RIT V G. Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns[J]. Biological Cybernetics, 1995, 73(4):357-366. [31] ADAMS M J, BRISCOE B J, JOHNSON S A. Friction and lubrication of human skin[J]. Tribology Letters, 2007, 26(3):239-253. [32] JOHNSON S A, GORMAN D M, ADAMS M J, et al. The friction and lubrication of human stratum corneum[M]//DOWSON D, TAYLOR C M, CHILDS T H C, et al. Tribology Series. Amsterdam:Elsevier, 1993, 25:663-672. [33] GORYACHEVA I, MAKHOVSKAYA Y. Combined effect of surface microgeometry and adhesion in normal and sliding contacts of elastic bodies[J]. Friction, 2017, 5(3):339-350. [34] ADAMS M J, BRISCOE B J, WEE T K. The differential friction effect of keratin fibres[J]. Journal of Physics D:Applied Physics, 1990, 23(4):406-414. [35] LYNN B. The form and distribution of the receptive fields of Pacinian corpuscles found in and around the cat's large foot pad[J]. The Journal of Physiology, 1971, 217(3):755-771. [36] SUTTON S, BRAREN M, ZUBIN J, et al. Evoked-potential correlates of stimulus uncertainty[J]. Science, 1965, 150(3700):1187-1188. [37] D'ANNIBALE F, CASALOTTI A, LUONGO A. Stick-slip and wear phenomena at the contact interface between an elastic beam and a rigid substrate[J]. Mathematics and Mechanics of Solids, 2021, 26(6):843-860. [38] WENDLING F, BELLANGER J J, BARTOLOMEI F, et al. Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals[J]. Biological Cybernetics, 2000, 83(4):367-378.