Detection of static, dynamic, and no tactile friction based on nonlinear dynamics of EEG signals: A preliminary study

Golnaz Baghdadi and Mahmood Amiri

Chaos, Solitons & Fractals, 2021, vol. 142, issue C

Abstract: Touching an object leads to the formation of frictional interaction between skin and object surface. Through this frictional interaction, useful information about the different characteristics of the object is obtained. There are two types of friction: static and dynamic. In this study, we designed a new haptic experiment and then investigated the effect of both friction and no contact situation on brain dynamics. Each trial consisted of three states. In the first and second states, participants experienced static and dynamic friction, respectively, while in the third, there was no contact between the skin and the surface. During the experiment, EEG signals were recorded from participants. Next, different linear and nonlinear EEG indices were extracted and analyzed to explore the effect of static and dynamic tactile friction on brain electrical responses. The results show that linear indices, such as spectral features, are almost identical among three states. However, nonlinear features, including Lyapunov exponent, Higuchi's dimension, and Hurst exponent, can distinguish these states. During dynamic friction, the sign of predictability (i.e., negative Lyapunov exponent) is observed while the existence of long-range dependency/memory (i.e., Hurst exponent > 0.5) is seen during all states. Furthermore, it is found that the complexity of the tactile sense in the Theta band is higher than the Delta band. This research demonstrates that applying nonlinear methods can reveal more characteristics of brain responses in haptic experiments. Finally, a preliminary system is proposed that can automatically detect the friction between skin and surface.

Keywords: Tactile friction; Classification; EEG nonlinear features; Lyapunov exponent; Higuchi's dimension; Hurst exponent (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:142:y:2021:i:c:s0960077920308419

DOI: 10.1016/j.chaos.2020.110449

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